@inproceedings{lerman2022social,
  title={The Social Emotional Web},
  author={Lerman, Kristina},
  booktitle={Proceedings of the IEEE International Conference on Collaboration and Internet Computing (IEEE CIC)},
  year={2022}
}

@article{lerman2022gendered,
  title={Gendered citation patterns among the scientific elite},
  author={Lerman, Kristina and Yu, Yulin and Morstatter, Fred and Pujara, Jay},
  journal={Proceedings of the National Academy of Sciences},
  volume={119},
  number={40},
  pages={e2206070119},
  year={2022},
  publisher={National Acad Sciences}
}

@inproceedings{mokhberian2022noise,
  title={Noise Audits Improve Moral Foundation Classification},
  author={Mokhberian, Negar and Hopp, Frederic R and Harandizadeh, Bahareh and Morstatter, Fred and Lerman, Kristina},
  booktitle={Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  year={2022}
}

@article{burghardt2022road,
  title={Road network evolution in the urban and rural United States since 1900},
  author={Burghardt, Keith and Uhl, Johannes H and Lerman, Kristina and Leyk, Stefan},
  journal={Computers, Environment and Urban Systems},
  volume={95},
  pages={101803},
  year={2022},
  publisher={Pergamon}
}

@inproceedings{sun2022assessing,
  title={Assessing Scientific Research Papers with Knowledge Graphs},
  author={Sun, Kexuan and Qiu, Zhiqiang and Salinas, Abel and Huang, Yuzhong and Lee, Dong-Ho and Benjamin, Daniel and Morstatter, Fred and Ren, Xiang and Lerman, Kristina and Pujara, Jay},
  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={2467--2472},
  year={2022}
}

@article{muric2022large,
  title={Large-scale agent-based simulations of online social networks},
  author={Muri{\'c}, Goran and Tregubov, Alexey and Blythe, Jim and Abeliuk, Andr{\'e}s and Choudhary, Divya and Lerman, Kristina and Ferrara, Emilio},
  journal={Autonomous Agents and Multi-Agent Systems},
  volume={36},
  number={2},
  pages={1--21},
  year={2022},
  publisher={Springer US}
}

@inproceedings{he2022learning,
  title={Learning fairer interventions},
  author={He, Yuzi and Burghardt, Keith and Guo, Siyi and Lerman, Kristina},
  booktitle={Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={317--323},
  year={2022}
}

@article{yau2022tiles,
  title={TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit},
  author={Yau, Joanna C and Girault, Benjamin and Feng, Tiantian and Mundnich, Karel and Nadarajan, Amrutha and Booth, Brandon M and Ferrara, Emilio and Lerman, Kristina and Hsieh, Eric and Narayanan, Shrikanth},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={1--17},
  year={2022},
  publisher={Nature Publishing Group}
}

@article{he2022leveraging,
  title={Leveraging change point detection to discover natural experiments in data},
  author={He, Yuzi and Burghardt, Keith A and Lerman, Kristina},
  journal={EPJ Data Science},
  volume={11},
  number={1},
  pages={49},
  year={2022},
  publisher={Springer Berlin Heidelberg}
}

@article{burghardt2022city,
  title={City definition affects long-term urban scaling analyses in the United States (1900-2015)},
  author={Burghardt, Keith and Uhl, Johannes H and Lerman, Kristina and Leyk, Stefan},
  journal={arXiv preprint arXiv:2209.10852},
  year={2022}
}

@article{guo2022emotion,
  title={Emotion Regulation and Dynamics of Moral Concerns During the Early COVID-19 Pandemic},
  author={Guo, Siyi and Burghardt, Keith and Rao, Ashwin and Lerman, Kristina},
  journal={arXiv preprint arXiv:2203.03608},
  year={2022}
}

@article{he2022infusing,
  title={Infusing Knowledge from Wikipedia to Enhance Stance Detection},
  author={He, Zihao and Mokhberian, Negar and Lerman, Kristina},
  journal={arXiv preprint arXiv:2204.03839},
  year={2022}
}

@article{rao2022partisan,
  title={Partisan Asymmetries in Exposure to Misinformation},
  author={Rao, Ashwin and Morstatter, Fred and Lerman, Kristina},
  journal={arXiv preprint arXiv:2203.01350},
  year={2022}
}

@article{burghardt2022road,
  title={Road network evolution in the urban and rural United States since 1900},
  author={Burghardt, Keith and Uhl, Johannes H and Lerman, Kristina and Leyk, Stefan},
  journal={Computers, Environment and Urban Systems},
  volume={95},
  pages={101803},
  year={2022},
  publisher={Elsevier}
}

@article{nettasinghe2022scale,
  title={Scale-free degree distributions, homophily and the glass ceiling effect in directed networks},
  author={Nettasinghe, Buddhika and Alipourfard, Nazanin and Iota, Stephen and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={Journal of Complex Networks},
  volume={10},
  number={2},
  pages={cnac007},
  year={2022},
  publisher={Oxford University Press}
}

@article{burghardt2022unequal,
  title={Unequal impact and spatial aggregation distort covid-19 growth rates},
  author={Burghardt, Keith and Guo, Siyi and Lerman, Kristina},
  journal={Philosophical Transactions of the Royal Society A},
  volume={380},
  number={2214},
  pages={20210122},
  year={2022},
  publisher={The Royal Society}
}

@article{priniski2021mapping,
  title={Mapping Moral Valence of Tweets Following the Killing of George Floyd},
  author={Priniski, J Hunter and Mokhberian, Negar and Harandizadeh, Bahareh and Morstatter, Fred and Lerman, Kristina and Lu, Hongjing and Brantingham, P Jeffrey},
  journal={arXiv preprint arXiv:2104.09578},
  year={2021}
}

@article{burghardt2021emergence,
  title={The emergence of heterogeneous scaling in research institutions},
  author={Burghardt, Keith A and He, Zihao and Percus, Allon G and Lerman, Kristina},
  journal={Communications Physics},
  volume={4},
  number={1},
  pages={1--6},
  year={2021},
  publisher={Nature Publishing Group}
}

@inproceedings{he2021detecting,
  title={Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings},
  author={He, Zihao and Mokhberian, Negar and C{\^a}mara, Ant{\'o}nio and Abeliuk, Andres and Lerman, Kristina},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
  pages={2102--2118},
  year={2021}
}

@inproceedings{he2021speaker,
  title={Speaker Turn Modeling for Dialogue Act Classification},
  author={He, Zihao and Tavabi, Leili and Lerman, Kristina and Soleymani, Mohammad},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
  pages={2150--2157},
  year={2021}
}

@article{alipourfard2021DoGR,
  title={Disaggregation via Gaussian regression for robust analysis of heterogeneous data},
  author={Alipourfard, Nazanin and Burghardt, Keith and Lerman, Kristina},
  journal={Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods},
  year={2021},
  publisher={Routledge}
}

@inproceedings{yan2021mitigating,
  title={Mitigating the Bias of Heterogeneous Human Behavior in Affective Computing},
  author={Yan, Shen and Kao, Hsien-Te and Lerman, Kristina and Narayanan, Shrikanth and Ferrara, Emilio},
  booktitle={2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}

@article{espin2021explaining,
  title={Explaining classification performance and bias via network structure and sampling technique},
  author={Esp{\'\i}n-Noboa, Lisette and Karimi, Fariba and Ribeiro, Bruno and Lerman, Kristina and Wagner, Claudia},
  journal={Applied Network Science},
  volume={6},
  number={1},
  pages={1--25},
  year={2021},
  publisher={SpringerOpen}
}

@article{mehrabi2021survey,
  title={A survey on bias and fairness in machine learning},
  author={Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
  journal={ACM Computing Surveys (CSUR)},
  volume={54},
  number={6},
  pages={1--35},
  year={2021},
  doi={10.1145/3457607},
  publisher={ACM New York, NY, USA}
}

@article{nettasinghe2021directed,
  title={A Directed, Bi-Populated Preferential Attachment Model with Applications to Analyzing the Glass Ceiling Effect},
  author={Nettasinghe, Buddhika and Alipourfard, Nazanin and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={arXiv preprint arXiv:2103.12149},
  year={2021}
}

@article{nettasinghe2021emergence,
  title={Emergence of Structural Inequalities in Scientific Citation Networks},
  author={Nettasinghe, Buddhika and Alipourfard, Nazanin and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={arXiv preprint arXiv:2103.10944},
  year={2021}
}

@article{rao2021political,
  title={Political Partisanship and Antiscience Attitudes in Online Discussions About COVID-19: Twitter Content Analysis},
  author={Rao, Ashwin and Morstatter, Fred and Hu, Minda and Chen, Emily and Burghardt, Keith and Ferrara, Emilio and Lerman, Kristina and others},
  journal={Journal of Medical Internet Research},
  volume={23},
  number={6},
  pages={e26692},
  year={2021},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@article{hu2021socioeconomic,
  title={Socioeconomic Correlates of Anti-Science Attitudes in the US},
  author={Hu, Minda and Rao, Ashwin and Kejriwal, Mayank and Lerman, Kristina},
  journal={Future Internet},
  volume={13},
  number={6},
  pages={160},
  year={2021},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{muric2021gender,
  title={Gender disparity in the authorship of biomedical research publications during the COVID-19 pandemic: Retrospective observational study},
  author={Muric, Goran and Lerman, Kristina and Ferrara, Emilio},
  journal={Journal of medical Internet research},
  volume={23},
  number={4},
  pages={e25379},
  year={2021},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@inproceedings{burghardt2021having,
  title={Having a Bad Day? Detecting the Impact of Atypical Events Using Wearable Sensors},
  author={Burghardt, Keith and Tavabi, Nazgol and Ferrara, Emilio and Narayanan, Shrikanth and Lerman, Kristina},
  booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation},
  pages={257--267},
  year={2021},
  organization={Springer}
}

@inproceedings{he2021identifying,
  title={Identifying Shifts in Collective Attention to Topics on Social Media},
  author={He, Yuzi and Rao, Ashwin and Burghardt, Keith and Lerman, Kristina},
  booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation},
  pages={224--234},
  year={2021},
  organization={Springer}
}

@inproceedings{bartley2021auditing,
  title={Auditing Algorithmic Bias on Twitter},
  author={Bartley, Nathan and Abeliuk, Andres and Ferrara, Emilio and Lerman, Kristina},
  booktitle={13th ACM Web Science Conference 2021},
  pages={65--73},
  year={2021}
}

@inproceedings{santos2021limiting,
  title={Limiting Tags Fosters Efficiency},
  author={Santos, Tiago and Burghardt, Keith and Lerman, Kristina and Helic, Denis},
  booktitle={13th ACM Web Science Conference 2021},
  pages={46--55},
  year={2021}
}

@inproceedings{he2021heterogeneous,
  title={Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game},
  author={He, Yuzi and Tran, Christopher and Jiang, Julie and Burghardt, Keith and Ferrara, Emilio and Zheleva, Elena and Lerman, Kristina},
  booktitle={The 16th International Conference on the Foundations of Digital Games (FDG) 2021},
  pages={1--9},
  year={2021}
}

@article{jiang2021wide,
  title={The Wide, the Deep, and the Maverick: Types of Players in Team-based Online Games},
  author={Jiang, Julie and Maldeniya, Danaja and Lerman, Kristina and Ferrara, Emilio},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={5},
  number={CSCW1},
  pages={1--26},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@article{soni2021follow,
  title={Follow the leader: Documents on the leading edge of semantic change get more citations},
  author={Soni, Sandeep and Lerman, Kristina and Eisenstein, Jacob},
  journal={Journal of the Association for Information Science and Technology},
  volume={72},
  number={4},
  pages={478--492},
  year={2021},
  publisher={Wiley Online Library}
}

@article{chen2021covid,
  title={COVID-19 misinformation and the 2020 US presidential election},
  author={Chen, Emily and Chang, Herbert and Rao, Ashwin and Lerman, Kristina and Cowan, Geoffrey and Ferrara, Emilio},
  journal={The Harvard Kennedy School Misinformation Review},
  year={2021},
  publisher={Shorenstein Center for Media, Politics and Public Policy, at Harvard~�}
}

@inproceedings{tavabi2020learning,
  title={Learning Behavioral Representations from Wearable Sensors},
  author={Tavabi, Nazgol and Hosseinmardi, Homa and Villatte, Jennifer L and Abeliuk, Andr{\'e}s and Narayanan, Shrikanth and Ferrara, Emilio and Lerman, Kristina},
  booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation},
  pages={245--254},
  year={2020},
  organization={Springer}
}

@inproceedings{mokhberian2020moral,
  title={Moral Framing and Ideological Bias of News},
  author={Mokhberian, Negar and Abeliuk, Andr{\'e}s and Cummings, Patrick and Lerman, Kristina},
  booktitle={International Conference on Social Informatics},
  pages={206--219},
  year={2020},
  organization={Springer}
}

@article{mundnich2020tiles,
  title={TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers},
  author={Mundnich, Karel and Booth, Brandon M and l�Hommedieu, Michelle and Feng, Tiantian and Girault, Benjamin and L�hommedieu, Justin and Wildman, Mackenzie and Skaaden, Sophia and Nadarajan, Amrutha and Villatte, Jennifer L and others},
  journal={Scientific Data},
  volume={7},
  number={1},
  pages={1--26},
  year={2020},
  publisher={Nature Publishing Group}
}

@Article{Chen2020,
author="Chen, Emily and Lerman, Kristina and Ferrara, Emilio",
title="Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set",
journal="JMIR Public Health Surveill",
year="2020",
month="May",
day="29",
volume="6",
number="2",
pages="e19273",
keywords="COVID-19; SARS-CoV-2; social media; network analysis; computational social sciences",
issn="2369-2960",
doi="10.2196/19273",
url="http://publichealth.jmir.org/2020/2/e19273/",
url="https://doi.org/10.2196/19273",
url="http://www.ncbi.nlm.nih.gov/pubmed/32427106"
}

@ARTICLE{jiang2020political,
  author =       {Julie Jiang and Emily Chen and Shen Yan and Kristina Lerman and
Emilio Ferrara},
  title =        {Political polarization drives online conversations about
COVID-19 in the United States},
  journal =      {Human Behavior and Emerging Technology},
  year =         {2020},
  volume =       {2},
  number =       {},
  pages =        {200-211},
  month =        {jul},
  note =         {},
  abstract =     {Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversations. By linking
2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions
about COVID-19. We show that COVID-19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Crossideological interactions are modulated by user segregation and polarized network
structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states.
These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online platforms to measure the effectiveness of public health
campaigns, and to monitor the reception of national and state-level policies, by tracking in real-time discussions in a highly polarized social media ecosystem},
  keywords =     {covid19},
  doi =          {10.1002/hbe2.202},
}

Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID-19. We show that COVID-19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Crossideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states. These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state-level policies, by tracking in real-time discussions in a highly polarized social media ecosystem

@inproceedings{huang2020graph,
  title={Graph embedding with personalized context distribution},
  author={Huang, Di and He, Zihao and Huang, Yuzhong and Sun, Kexuan and Abu-El-Haija, Sami and Perozzi, Bryan and Lerman, Kristina and Morstatter, Fred and Galstyan, Aram},
  booktitle={Companion Proceedings of the Web Conference 2020},
  pages={655--661},
  year={2020}
}

@article{wu2020transsortative,
  title={The transsortative structure of networks},
  author={Ngo, Shin-Chieng and Percus, Allon G and Burghardt, Keith and Lerman, Kristina},
  journal={Proceedings of the Royal Society A},
  volume={476},
  number={2237},
  pages={20190772},
  year={2020},
  publisher={The Royal Society Publishing}
}

@inproceedings{muric2020massive,
  title={Massive Cross-Platform Simulations of Online Social Networks},
  author={Muri{\'c}, Goran and Tregubov, Alexey and Blythe, Jim and Abeliuk, Andr{\'e}s and Choudhary, Divya and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={895--903},
  year={2020}
}

@article{abeliuk2020predictability,
  title={Predictability limit of partially observed systems},
  author={Abeliuk, Andr{\'e}s and Huang, Zhishen and Ferrara, Emilio and Lerman, Kristina},
  journal={Scientific Reports},
  volume={10},
  number={20427},
  doi={10.1038/s41598-020-77091-1},
  year={2020}
}

@inproceedings{santos2020can,
  title={Can Badges Foster a More Welcoming Culture on Q\&A Boards?},
  author={Santos, Tiago and Burghardt, Keith and Lerman, Kristina and Helic, Denis},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={14},
  pages={969--973},
  year={2020}
}

@article{burghardt2020origins,
  title={Origins of Algorithmic Instabilities in Crowdsourced Ranking},
  author={Burghardt, Keith and Hogg, Tad and D'Souza, Raissa and Lerman, Kristina and Posfai, Marton},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={4},
  number={CSCW2},
  pages={1--20},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@inproceedings{he2020geometric,
  title={A Geometric Solution to Fair Representations},
  author={He, Yuzi and Burghardt, Keith and Lerman, Kristina},
  booktitle={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
  pages={279--285},
  year={2020}
}

@article{kao2020user,
  title={User-Based Collaborative Filtering Mobile Health System},
  author={Kao, Hsien-Te and Yan, Shen and Hosseinmardi, Homa and Narayanan, Shrikanth and Lerman, Kristina and Ferrara, Emilio},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={4},
  number={4},
  pages={1--17},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@ARTICLE{Mundnich2020,
  author =       {
Karel Mundnich and Brandon M Booth and Michelle l'Hommedieu and Tiantian Feng and Benjamin Girault and Justin L'Hommedieu and Mackenzie Wildman and Sophia Skaaden and Amrutha Nadarajan and Jennifer L Villatte and Tiago H Falk and Kristina Lerman and Emilio Ferrara and Shrikanth Narayanan},
  title =        {TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers},
  journal =      {Scientific Data},
  year =         {2020},
  volume =       {7},
  number =       {354},
  doi =          {10.1038/s41597-020-00655-3},
}

@article{yan2020affect,
  title={Affect Estimation with Wearable Sensors},
  author={Yan, Shen and Hosseinmardi, Homa and Kao, Hsien-Te and Narayanan, Shrikanth and Lerman, Kristina and Ferrara, Emilio},
  journal={Journal of Healthcare Informatics Research},
  pages={1--34},
  year={2020},
  publisher={Springer International Publishing}
}

@inproceedings{tavabi2020challenges,
  title={Challenges in Forecasting Malicious Events from Incomplete Data},
  author={Tavabi, Nazgol and Abeliuk, Andr{\'e}s and Mokhberian, Negar and Abramson, Jeremy and Lerman, Kristina},
  booktitle={Companion Proceedings of the Web Conference 2020},
  pages={603--610},
  year={2020}
}

@inproceedings{huang2020graph,
  title={Graph Embedding with Personalized Context Distribution},
  author={Huang, Di and He, Zihao and Huang, Yuzhong and Sun, Kexuan and Abu-El-Haija, Sami and Perozzi, Bryan and Lerman, Kristina and Morstatter, Fred and Galstyan, Aram},
  booktitle={Companion Proceedings of the Web Conference 2020},
  pages={655--661},
  year={2020}
}

@article{alipourfard2020friendship,
  title={Friendship paradox biases perceptions in directed networks},
  author={Alipourfard, Nazanin and Nettasinghe, Buddhika and Abeliuk, Andr{\'e}s and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={Nature communications},
  volume={11},
  number={1},
  pages={1--9},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{ambite2019bd2k,
  title={BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science},
  author={Ambite, Jose Luis and Fierro, Lily and Gordon, Jonathan and Burns, Gully and Geigl, Florian and Lerman, Kristina and Van Horn, John D},
  journal={IEEE Transactions on Emerging Topics in Computing},
  year={2019},
  publisher={IEEE}
}

@inproceedings{sayyadiharikandeh2019finding,
  title={Finding Prerequisite Relations using the Wikipedia Clickstream},
  author={Sayyadiharikandeh, Mohsen and Gordon, Jonathan and Ambite, Jose-Luis and Lerman, Kristina},
  booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
  pages={1240--1247},
  year={2019}
}

@article{nettasinghe2019diffusion,
  title={Diffusion in social networks: Effects of monophilic contagion, friendship paradox and reactive networks},
  author={Nettasinghe, Buddhika and Krishnamurthy, Vikram and Lerman, Kristina},
  journal={IEEE Transactions on Network Science and Engineering},
  year={2019},
  publisher={IEEE}
}

@article{abu2019mixhop,
  title={Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing},
  author={Abu-El-Haija, Sami and Perozzi, Bryan and Kapoor, Amol and Alipourfard, Nazanin and Lerman, Kristina and Harutyunyan, Hrayr and Steeg, Greg Ver and Galstyan, Aram},
  journal={arXiv preprint arXiv:1905.00067},
  year={2019}
}

@inproceedings{blythe2019darpa,
  title={The DARPA SocialSim Challenge: Massive Multi-Agent Simulations of the Github Ecosystem},
  author={Blythe, James and Ferrara, Emilio and Huang, Di and Lerman, Kristina and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Pacheco, Diogo and Bollenbacher, John and Flammini, Alessandro and others},
  booktitle={Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={1835--1837},
  year={2019},
  organization={International Foundation for Autonomous Agents and Multiagent Systems}
}

@inproceedings{krohn2019massive,
  title={Massive Multi-agent Data-Driven Simulations of the GitHub Ecosystem},
  author={Krohn, Rachel and Pacheco, Diogo and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Ahn, Yong-Yeol and Flammini, Alessandro and Lerman, Kristina and Menczer, Filippo and Weninger, Tim and others},
  booktitle={Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection: 17th International Conference, PAAMS 2019, {\'A}vila, Spain, June 26-28, 2019, Proceedings},
  volume={11523},
  pages={3},
  year={2019},
  organization={Springer}
}

@inproceedings{blythe2019massive,
  title={Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem},
  author={Blythe, Jim and Bollenbacher, John and Huang, Di and Hui, Pik-Mai and Krohn, Rachel and Pacheco, Diogo and Muric, Goran and Sapienza, Anna and Tregubov, Alexey and Ahn, Yong-Yeol and others},
  booktitle={International Conference on Practical Applications of Agents and Multi-Agent Systems},
  pages={3--15},
  year={2019},
  organization={Springer, Cham}
}

@article{fennell2019predicting,
  title={Predicting and explaining behavioral data with structured feature space decomposition},
  author={Fennell, Peter G and Zuo, Zhiya and Lerman, Kristina},
  journal={EPJ Data Science},
  volume={8},
  number={1},
  pages={23},
  year={2019},
  doi={https://doi.org/10.1140/epjds/s13688-019-0201-0},
  publisher={Springer Berlin Heidelberg}
}

@inproceedings{morstatter2019sage,
  title={SAGE: a hybrid geopolitical event forecasting system},
  author={Morstatter, Fred and Galstyan, Aram and Satyukov, Gleb and Benjamin, Daniel and Abeliuk, Andres and Mirtaheri, Mehrnoosh and Hossain, KSM and Szekely, Pedro and Ferrara, Emilio and Matsui, Akira and others},
  booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
  pages={6557--6559},
  year={2019},
  organization={AAAI Press}
}

@article{muric2019collaboration,
  title={Collaboration Drives Individual Productivity},
  author={Muri{\'c}, Goran and Abeliuk, Andres and Lerman, Kristina and Ferrara, Emilio},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={3},
  number={CSCW},
  pages={1--24},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{yan2019estimating,
  title={Estimating individualized daily self-reported affect with wearable sensors},
  author={Yan, Shen and Hosseinmardi, Homa and Kao, Hsien-Te and Narayanan, Shrikanth and Lerman, Kristina and Ferrara, Emilio},
  booktitle={2019 IEEE International Conference on Healthcare Informatics (ICHI)},
  pages={1--9},
  year={2019},
  organization={IEEE}
}

@article{l2019lessons,
  title={Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization},
  author={L'Hommedieu, Michelle and L'Hommedieu, Justin and Begay, Cynthia and Schenone, Alison and Dimitropoulou, Lida and Margolin, Gayla and Falk, Tiago and Ferrara, Emilio and Lerman, Kristina and Narayanan, Shrikanth},
  journal={JMIR mHealth and uHealth},
  volume={7},
  number={12},
  pages={e13305},
  year={2019},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@inproceedings{Addawood2019linguistic,
  title={Linguistic Cues to Deception: Identifying Political Trolls on Social Media},
  author={Addawood, Aseel and Badawy, Adam and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={13},
  number={01},
  pages={15--25},
  year={2019}
}

@inproceedings{Badawy2019falls,
  title={Who Falls for Online Political Manipulation?},
  author={Badawy, Adam and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
  pages={162--168},
  year={2019},
  organization={ACM}
}

@article{Badawy2019characterizing,
  title={Characterizing the 2016 Russian IRA influence campaign},
  author={Badawy, Adam and Addawood, Aseel and Lerman, Kristina and Ferrara, Emilio},
  journal={Social Network Analysis and Mining},
  volume={9},
  number={1},
  pages={31},
  year={2019},
  publisher={Springer}
}

@INPROCEEDINGS{Sayyadiharikandeh2019finding,
  author =       {Mohsen Sayyadiharikandeh and Jonathan Gordon and Jose Luis Ambite and Kristina Lerman},
  title =        {Finding Prerequisite Relations using the Wikipedia Clickstream},
  booktitle =    {Proceedings of the Companion to The Web Conference: CyberSafety Workshop},
  year =         {2019},
  keywords =     {},
}

@INPROCEEDINGS{Tavabi2019characterizing,
  author =       {Nazgol Tavabi and Nathan Bartley and Andres Abeliuk and Sandeep Soni and Emilio Ferrara and Kristina Lerman},
  title =        {Characterizing Activity on the Deep and DarkWeb},
   booktitle =    {Proceedings of the Companion to The Web Conference: CyberSafety Workshop},
  year =         {2019},
 }

@inproceedings{Badawy2018analyzing,
  title={Analyzing the digital traces of political manipulation: the 2016 Russian interference Twitter campaign},
  author={Badawy, Adam and Ferrara, Emilio and Lerman, Kristina},
  booktitle={2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  pages={258--265},
  year={2018},
  organization={IEEE}
}

@INPROCEEDINGS{Kao2018besc,
  author =       {Hsien-Te Kao and Homa Hosseinmardi and Shen Yan and Michelle Hasan and Shrikanth Narayanan and  Kristina Lerman and Emilio Ferrara},
  title =        {Discovering Latent Psychological Structures from Self-report Assessments of Hospital Workers},
  booktitle =    {The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing},
  year =         {2018},
  keywords =     {behavioral-modeling},
}

@article{Wu2018pre,
  title = {Degree correlations amplify the growth of cascades in networks},
  author = {Wu, Xin-Zeng and Fennell, Peter G. and Percus, Allon G. and Lerman, Kristina},
  journal = {Phys. Rev. E},
  volume = {98},
  issue = {2},
  pages = {022321},
  numpages = {8},
  year = {2018},
  month = {Aug},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.98.022321},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.98.022321},
  keywords =     {social-networks},
}

@inproceedings{Badawy2018asonam,
    abstract = {Until recently, social media was seen to promote democratic discourse on
social and political issues. However, this powerful communication platform has
come under scrutiny for allowing hostile actors to exploit online discussions
in an attempt to manipulate public opinion. A case in point is the ongoing {U.S}.
Congress' investigation of Russian interference in the 2016 {U.S}. election
campaign, with Russia accused of using trolls (malicious accounts created to
manipulate) and bots to spread misinformation and politically biased
information. In this study, we explore the effects of this manipulation
campaign, taking a closer look at users who re-shared the posts produced on
Twitter by the Russian troll accounts publicly disclosed by {U.S}. Congress
investigation. We collected a dataset with over 43 million election-related
posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
million distinct users. This dataset included accounts associated with the
identified Russian trolls. We use label propagation to infer the ideology of
all users based on the news sources they shared. This method enables us to
classify a large number of users as liberal or conservative with precision and
recall above 90\%. Conservatives retweeted Russian trolls about 31 times more
often than liberals and produced 36x more tweets. Additionally, most retweets
of troll content originated from two Southern states: Tennessee and Texas.
Using state-of-the-art bot detection techniques, we estimated that about 4.9\%
and 6.2\% of liberal and conservative users respectively were bots. Text
analysis on the content shared by trolls reveals that they had a mostly
conservative, {pro-Trump} agenda. Although an ideologically broad swath of
Twitter users was exposed to Russian Trolls in the period leading up to the
2016 {U.S}. Presidential election, it was mainly conservatives who helped amplify
their message.},
    booktitle = {Proceedings of The international conference series on Advances in Social Network Analysis and Mining  (ASONAM-2018)},
    title = {Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign},
    url = {http://arxiv.org/abs/1802.04291},
    year = {2018},
    keywords = {fake-news}
}

Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress' investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of using trolls (malicious accounts created to manipulate) and bots to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million election-related posts shared on Twitter between September 16 and October 21, 2016, by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36x more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users was exposed to Russian Trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message.

@Article{Hodas2018jcss,
author="Hodas, Nathan O. and Hunter, Jacob and Young, Stephen J. and Lerman, Kristina",
title="Model of cognitive dynamics predicts performance on standardized tests",
journal="Journal of Computational Social Science",
year="2018",
month="Aug",
day="14",
abstract="In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its depletion, cognitive functions such as self-control and decision-making may decline. While fatigue, among other factors, affects human activity, how cognitive performance evolves during extended periods of focus remains poorly understood. By analyzing performance of a large cohort answering practice standardized test questions online, we show that accuracy and learning decline as the test session progresses and recover following prolonged breaks. To explain these findings, we hypothesize that answering questions consumes some finite cognitive resources on which performance depends, but these resources recover during breaks between test questions. We propose a dynamic mechanism of the consumption and recovery of these resources and show that it explains empirical findings and predicts performance better than alternative hypotheses. While further controlled experiments are needed to identify the physiological origin of these phenomena, our work highlights the potential of empirical analysis of large-scale human behavior data to explore cognitive behavior.",
issn="2432-2725",
doi="10.1007/s42001-018-0025-x",
url="https://doi.org/10.1007/s42001-018-0025-x",
abstract = "In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its depletion, cognitive functions such as self-control and decision-making may decline. While fatigue, among other factors, affects human activity, how cognitive performance evolves during extended periods of focus remains poorly understood. By analyzing performance of a large cohort answering practice standardized test questions online, we show that accuracy and learning decline as the test session progresses and recover following prolonged breaks. To explain these findings, we hypothesize that answering questions consumes some finite cognitive resources on which performance depends, but these resources recover during breaks between test questions. We propose a dynamic mechanism of the consumption and recovery of these resources and show that it explains empirical findings and predicts performance better than alternative hypotheses. While further controlled experiments are needed to identify the physiological origin of these phenomena, our work highlights the potential of empirical analysis of large-scale human behavior data to explore cognitive behavior.",
}

In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its depletion, cognitive functions such as self-control and decision-making may decline. While fatigue, among other factors, affects human activity, how cognitive performance evolves during extended periods of focus remains poorly understood. By analyzing performance of a large cohort answering practice standardized test questions online, we show that accuracy and learning decline as the test session progresses and recover following prolonged breaks. To explain these findings, we hypothesize that answering questions consumes some finite cognitive resources on which performance depends, but these resources recover during breaks between test questions. We propose a dynamic mechanism of the consumption and recovery of these resources and show that it explains empirical findings and predicts performance better than alternative hypotheses. While further controlled experiments are needed to identify the physiological origin of these phenomena, our work highlights the potential of empirical analysis of large-scale human behavior data to explore cognitive behavior.

@article{Sapienza2018individual,
  title={Individual performance in team-based online games},
  author={Sapienza, Anna and Zeng, Yilei and Bessi, Alessandro and Lerman, Kristina and Ferrara, Emilio},
  journal={Royal Society Open Science},
  volume={5},
  number={6},
  pages={180329},
  year={2018},
  publisher={The Royal Society}
}

@INPROCEEDINGS{Burghardt2018icwsm,
  author =       {Keith Burghardt and Tad Hogg and Kristina Lerman},
  title =        {Quantifying the Impact of Cognitive Biases in Question-Answering Systems},
  booktitle =    {Proceedings of the 12th International AAAI Conference On Web And Social Media (ICWSM2018)},
  year =         {2018},
  publisher =    {AAAI},
  abstract =     {Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases.We investigate some of these biases in an experimental model of a question-answering system. We observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.},
  keywords =     {social-dynamics},
}

Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases.We investigate some of these biases in an experimental model of a question-answering system. We observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.

@inproceedings{momeni2018modeling,
  title={Modeling Evolution of Topics in Large-Scale Temporal Text Corpora},
  author={Momeni, Elaheh and Karunasekera, Shanika and Goyal, Palash and Lerman, Kristina},
  booktitle={Twelfth International AAAI Conference on Web and Social Media},
  year={2018}
}

@INPROCEEDINGS{Alipourfard2018icwsm,
  author =       {Nazanin Alipourfard and Peter G. Fennell and Kristina Lerman},
  title =        {Using Simpson�s Paradox to Discover Interesting Patterns in Behavioral Data},
  booktitle =    {Proceedings of the 12th International AAAI Conference On Web And Social Media (ICWSM2018)},
  year =         {2018},
  publisher =    {AAAI},
  abstract =     {We describe a data-driven discovery method that leverages Simpson�s paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trends to identify disaggregations that produce subgroups with different behaviors from the aggregate. We illustrate the method by applying it to three real-world behavioral datasets, including Q&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.},
  keywords =     {data-analysis},
  url   =        {https://arxiv.org/abs/1805.03094},
}

We describe a data-driven discovery method that leverages Simpson�s paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trends to identify disaggregations that produce subgroups with different behaviors from the aggregate. We illustrate the method by applying it to three real-world behavioral datasets, including Q&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.

@INPROCEEDINGS{Espin2018manet,
  author =       {Lisette Espin Noboa, Claudia Wagner, Fariba Karimi, Kristina Lerman},
  title =        {Towards Quantifying Sampling Bias in Networks},
  booktitle =    {Proceedings of the International workshop on Mining Attributed Networks WWW�2018 },
  year =         {2018},
  }

@INPROCEEDINGS{Sapienza2018www,
  author =       {Anna Sapienza and Sindhu Kiranmai Ernala and
               Alessandro Bessi and
               Kristina Lerman and
               Emilio Ferrara},
  title =        {DISCOVER: Mining Online Chatter for Emerging Cyber Threats},
  booktitle =    {Proceedings of WWW Companion for the Third International Workshop on Computational Methods for CyberSafety},
  year =         {2018},
  }

@INPROCEEDINGS{Tavabi2018iaai,
  author =       {Nazgol Tavabi and Palash Goyal and Mohammed Almukaynizi and Paulo Shakarian and Kristina Lerman},
  title =        {DarkEmbed: Exploit Prediction with Neural Language Models},
  booktitle =    {Proceedings of AAAI Conference on Innovative Applications of AI (IAAI2018)},
  year =         {2018},
  url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17304}
  }

@INPROCEEDINGS{Alipourfard2018wsdm,
  author =       {Nazanin Alipourfard and Peter Fennell and Kristina Lerman},
  title =        {Can you Trust the Trend? Discovering Simpson�s Paradoxes in Social Data},
  booktitle =    {Proceedings of the 11th International ACM Conference on Web Search and Data Mining (WSDM)},
  year =         {2018},
  pages =        {},
  doi={10.1145/3159652.3159684},
  publisher =    {ACM},
  abstract =     {We investigate how Simpson�s paradox affects analysis of trends
in social data. According to the paradox, the trends observed in
data that has been aggregated over an entire population may be
quite different from, and even opposite to, those of the underlying
subgroups. Failure to take this effect into account can lead
analysis to wrong conclusions. We present a statistical method to
automatically identify Simpson�s paradox in data by comparing
statistical trends in the aggregate data to those in the disaggregated
subgroups. We apply the approach to data from Stack Exchange, a
popular question-answering platform, to analyze factors affecting
answerer performance, specifically, the likelihood that an answer
provided by a user will be accepted by the asker as the best answer
to his or her question. Our analysis confirms a known Simpson�s
paradox and identifies several new instances. These paradoxes provide
novel insights into user behavior on Stack Exchange.},
  keywords =     {social-dynamics},
}

We investigate how Simpson�s paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire population may be quite different from, and even opposite to, those of the underlying subgroups. Failure to take this effect into account can lead analysis to wrong conclusions. We present a statistical method to automatically identify Simpson�s paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups. We apply the approach to data from Stack Exchange, a popular question-answering platform, to analyze factors affecting answerer performance, specifically, the likelihood that an answer provided by a user will be accepted by the asker as the best answer to his or her question. Our analysis confirms a known Simpson�s paradox and identifies several new instances. These paradoxes provide novel insights into user behavior on Stack Exchange.

@Article{Lerman2018jcss,
author="Lerman, Kristina",
title="Computational social scientist beware: Simpson's paradox in behavioral data",
journal="Journal of Computational Social Science",
year="2018",
month="Jan",
day="01",
volume="1",
number="1",
pages="49--58",
abstract="Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson's paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson's paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson's paradox is affecting results of analysis. The presence of Simpson's paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies' findings.",
issn="2432-2725",
doi="10.1007/s42001-017-0007-4",
url="https://doi.org/10.1007/s42001-017-0007-4"
}

Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson's paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson's paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson's paradox is affecting results of analysis. The presence of Simpson's paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies' findings.

@article{allem2017identifying,
  title={Identifying Sentiment of Hookah-Related Posts on Twitter},
  author={Allem, Jon-Patrick and Ramanujam, Jagannathan and Lerman, Kristina and Chu, Kar-Hai and Cruz, Tess Boley and Unger, Jennifer B},
  journal={JMIR Public Health and Surveillance},
  volume={3},
  number={4},
  year={2017},
  publisher={JMIR Publications Inc.},
  url = {https://www.ncbi.nlm.nih.gov/pubmed/29046267},
}

@inproceedings{Sapienza2017,
  author    = {Anna Sapienza and
               Alessandro Bessi and
               Saranya Damodaran and
               Paulo Shakarian and
               Kristina Lerman and
               Emilio Ferrara},
  title     = {Early Warnings of Cyber Threats in Online Discussions},
  booktitle = {2017 {IEEE} International Conference on Data Mining Workshops, {ICDM}
               Workshops 2017, New Orleans, LA, USA, November 18-21, 2017},
  pages     = {667--674},
  year      = {2017},
  crossref  = {DBLP:conf/icdm/2017w},
  url       = {https://doi.org/10.1109/ICDMW.2017.94},
  doi       = {10.1109/ICDMW.2017.94},
  timestamp = {Thu, 11 Jan 2018 09:07:04 +0100},
  biburl    = {http://dblp.org/rec/bib/conf/icdm/SapienzaBDSLF17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@inproceedings{ambite2017bd2k,
  title={BD2K ERuDIte: the Educational Resource Discovery Index for Data Science},
  author={Ambite, Jos{\'e} Luis and Fierro, Lily and Geigl, Florian and Gordon, Jonathan and Burns, Gully APC and Lerman, Kristina and Van Horn, John D},
  booktitle={Proceedings of the 26th International Conference on World Wide Web Companion},
  pages={1203--1211},
  year={2017},
  organization={International World Wide Web Conferences Steering Committee}
}

@ARTICLE{Yan2017,
  author =       {Xiaoran Yan and Brian M. Sadler and Robert J. Drost and Paul L. Yu and Kristina Lerman},
  title =        {Graph Filters and the Z-Laplacian},
  journal =      {IEEE Journal of Selected Topics in Signal Processing},
  year =         {2017},
  volume =       {11},
  number =       {6},
  pages =        {774--784},
  abstract =     {In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph shift operators. As a generalization of the traditional graph Laplacian, the Z-Laplacian spans the space of all possible Z -matrices, i.e., real square matrices with nonpositive off-diagonal entries. We show that the Z -Laplacian can model general continuous-time dynamical processes, including information flows and epidemic spreading on a given graph. It is also closely related to general nonnegative graph filters in the discrete time domain. We showcase its flexibility by considering two applications. First, we consider a wireless communications networking problem modeled with a graph, where the framework can be applied to model the effects of the underlying communications protocol and traffic. Second, we examine a structural brain network from the perspective of low- to high-frequency connectivity.},
}

In network science, the interplay between dynamical processes and the underlying topologies of complex systems has led to a diverse family of models with different interpretations. In graph signal processing, this is manifested in the form of different graph shifts and their induced algebraic systems. In this paper, we propose the unifying Z-Laplacian framework, whose instances can act as graph shift operators. As a generalization of the traditional graph Laplacian, the Z-Laplacian spans the space of all possible Z -matrices, i.e., real square matrices with nonpositive off-diagonal entries. We show that the Z -Laplacian can model general continuous-time dynamical processes, including information flows and epidemic spreading on a given graph. It is also closely related to general nonnegative graph filters in the discrete time domain. We showcase its flexibility by considering two applications. First, we consider a wireless communications networking problem modeled with a graph, where the framework can be applied to model the effects of the underlying communications protocol and traffic. Second, we examine a structural brain network from the perspective of low- to high-frequency connectivity.

@inproceedings{yan2017multi,
  title={Multi-layer network composition under a unified dynamical process},
  author={Yan, Xiaoran and Teng, Shang-Hua and Lerman, Kristina},
  booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (SBP)},
  pages={315--321},
  year={2017},
  organization={Springer}
}

@ARTICLE{Lerman2017jcss,
author="Lerman, Kristina and Marin, Luciano G.
and Arora, Megha and de Lima, Lucas H. Costa  and Ferrara, Emilio and Garcia, David",
title="Language, demographics, emotions, and the structure of online social networks",
journal="Journal of Computational Social Science",
year="2017",
month="Oct",
day="31",
abstract="Social networks affect individuals' economic opportunities and well-being. However, few of the factors thought to shape networks---culture, language, education, and income---were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.",
issn="2432-2725",
doi="10.1007/s42001-017-0001-x",
url="https://doi.org/10.1007/s42001-017-0001-x"
}

Social networks affect individuals' economic opportunities and well-being. However, few of the factors thought to shape networks—culture, language, education, and income—were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.

@INPROCEEDINGS{Wu2017,
  author =       {Hao Wu and Kristina Lerman},
  title =        {Deep Context: A Neural Language Model for Large-scale Networked Documents },
  booktitle =    {Proceedings of International Joint Conference on AI (IJCAI)},
  year =         {2017},
  editor =       {},
}

@article{wu2017network,
  title={Network vector: distributed representations of networks with global context},
  author={Wu, Hao and Lerman, Kristina},
  journal={arXiv preprint arXiv:1709.02448},
  year={2017}
}

@article{Wu2017neighbor,
  title={Neighbor-Neighbor Correlations Explain Measurement Bias in Networks},
  author={Wu, Xin-Zeng and Percus, Allon G and Lerman, Kristina},
  journal={Scientific Reports},
  volume={7},
  number =       {5576},
  month =        {jul},
  year={2017},
  publisher={Nature Publishing Group},
  doi =          {doi:10.1038/s41598-017-06042-0},
}

@INPROCEEDINGS{Agarwal2017quitting,
  author =       {Tushar Agarwal and Keith Burghardt and Kristina Lerman},
  title =        {On Quitting: Performance and Practice in Online Game Play},
  booktitle =    {Proceedings of 11th AAAI International Conference on Web and Social Media},
  year =         {2017},
  editor =       {},
  publisher =    {AAAI},
  abstract =     {We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, i.e., sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and ``grit'': successful players are those who persist in their practice despite lower scores.
Finally, we train an $\epsilon$-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game.
Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.},
  keywords =     {social-behavior},
  url={https://arxiv.org/abs/1703.04696}
}

We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, i.e., sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and ``grit'': successful players are those who persist in their practice despite lower scores. Finally, we train an $ε$-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game. Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.

@INPROCEEDINGS{Ferrara2017dynamics,
  author =       {Emilio Ferrara and Nazanin Alipourfard and Keith Burghardt and Chiranth Gopal and Kristina Lerman},
  title =        {Dynamics of Content Quality in Collaborative Knowledge Production},
  booktitle =    {Proceedings of 11th AAAI International Conference on Web and Social Media},
  year =         {2017},
  editor =       {},
  publisher =    {AAAI},
  abstract =     {We explore the dynamics of user performance in collaborative knowledge production by studying the quality of answers to questions posted  on Stack Exchange. We propose four indicators of answer quality: answer length, the number of code lines and hyperlinks to external web content it contains, and whether it is accepted by the asker as the most helpful answer to the question. Analyzing millions of answers posted over the period from 2008 to 2014, we uncover regular short-term and long-term changes in quality. In the short-term,
quality deteriorates over the course of a single session, with each successive answer becoming shorter, with fewer code lines and links, and less likely to be accepted. In contrast, performance improves over the long-term, with more experienced users producing higher quality answers. These trends are not a consequence of data heterogeneity, but rather have a behavioral origin. Our findings highlight the complex interplay between short-term deterioration in performance, potentially due to mental fatigue or attention depletion, and long-term performance improvement due to learning and skill acquisition, and its impact on the quality of user-generated content.},
  keywords =     {social-behavior},
}

We explore the dynamics of user performance in collaborative knowledge production by studying the quality of answers to questions posted on Stack Exchange. We propose four indicators of answer quality: answer length, the number of code lines and hyperlinks to external web content it contains, and whether it is accepted by the asker as the most helpful answer to the question. Analyzing millions of answers posted over the period from 2008 to 2014, we uncover regular short-term and long-term changes in quality. In the short-term, quality deteriorates over the course of a single session, with each successive answer becoming shorter, with fewer code lines and links, and less likely to be accepted. In contrast, performance improves over the long-term, with more experienced users producing higher quality answers. These trends are not a consequence of data heterogeneity, but rather have a behavioral origin. Our findings highlight the complex interplay between short-term deterioration in performance, potentially due to mental fatigue or attention depletion, and long-term performance improvement due to learning and skill acquisition, and its impact on the quality of user-generated content.

@article{Burghardt2017myopia,
    abstract = {Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the  � wisdom of crowds� effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer's salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend on heuristics to a greater extent than voters when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic, and they are more likely to choose the answer after it has been accepted than before that answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers to a question increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grows.},
    author = {Burghardt, Keith and Alsina, Emanuel F. and Girvan, Michelle and Rand, William and Lerman, Kristina},
    citeulike-article-id = {14312610},
    citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pone.0173610},
    day = {16},
    doi = {10.1371/journal.pone.0173610},
    journal = {PLOS ONE},
    keywords = {cognitive-constraints, cognitive-load, lerman, question-answering, wisdom-of-crowds},
    month = mar,
    number = {3},
    pages = {e0173610+},
    posted-at = {2017-03-17 00:37:42},
    priority = {2},
    publisher = {Public Library of Science},
    title = {The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange},
    url = {http://dx.doi.org/10.1371/journal.pone.0173610},
    volume = {12},
    year = {2017}
}

Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the � wisdom of crowds� effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer's salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend on heuristics to a greater extent than voters when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic, and they are more likely to choose the answer after it has been accepted than before that answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers to a question increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grows.

@article{Kang2017effort,
 author = {Kang, Jeon-Hyung and Lerman, Kristina},
 title = {Effort Mediates Access to Information in Online Social Networks},
 journal = {ACM Transactions on the Web},
 issue_date = {March 2017},
 volume = {11},
 number = {1},
 month = mar,
 year = {2017},
 issn = {1559-1131},
 pages = {3:1--3:19},
 articleno = {3},
 numpages = {19},
 url = {http://doi.acm.org/10.1145/2990506},
 doi = {10.1145/2990506},
 acmid = {2990506},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {social-networks},
}

@article{Huang2017travel,
    abstract = { We studied the relationship between characteristics of business clusters and check-in activities based on Twitter check-in data in Los Angeles County, California. We not only performed statistical analysis to model how various factors of clusters influence the intensity of check-in activities, but also proposed a visualization framework to understand the relationships among clusters embedded in a network. We discovered new insights on strategies of promoting flourishing business clusters based on social media data. Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.},
    author = {Huang, Arthur and Gallegos, Luciano and Lerman, Kristina},
    citeulike-article-id = {14276713},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.trc.2016.12.019},
    doi = {10.1016/j.trc.2016.12.019},
    issn = {0968090X},
    journal = {Transportation Research Part C: Emerging Technologies},
    keywords = {cities, geo, lerman, sentiment, twitter, urban},
    month = apr,
    pages = {245--256},
    posted-at = {2017-02-10 18:07:48},
    priority = {2},
    title = {Travel analytics: Understanding how destination choice and business clusters are connected based on social media data},
    url = {http://dx.doi.org/10.1016/j.trc.2016.12.019},
    volume = {77},
    year = {2017}
}

We studied the relationship between characteristics of business clusters and check-in activities based on Twitter check-in data in Los Angeles County, California. We not only performed statistical analysis to model how various factors of clusters influence the intensity of check-in activities, but also proposed a visualization framework to understand the relationships among clusters embedded in a network. We discovered new insights on strategies of promoting flourishing business clusters based on social media data. Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.

@INPROCEEDINGS{Abeliuk2017www,
  author =       {Andr�s Abeliuk and Gerardo Berbeglia and Pascal Van Hentenryck and Tad Hogg and Kristina Lerman},
  title =        {Taming the Unpredictability of Cultural Markets with Social Influence},
  booktitle =    {Proceedings of the 26th International World Wide Web Conference (WWW2017)},
  year =         {2017},
  pages =        {},
  abstract =     {Unpredictability is often portrayed as an undesirable outcome of social in uence in cultural markets. Unpredictability stems from the ``rich get richer'' effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social in uence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of social influence. We investigate strategies for creating markets in which the popularity of products is better aligned with their underlying quality. For our study, we created a cultural market of science stories and conducted randomized experiments on different policies for presenting the stories to study participants. Specically, we varied how the stories were ranked, and whether or not participants were shown the ratings these stories received from others. We present a policy that leverages social influence and product positioning to help distinguish the product's market share (popularity) from underlying quality. Highlighting products with the highest estimated quality reduces the ``rich get richer'' effect of using popularity directly. We show that this policy allows us to more robustly and predictably identify high quality products and promote blockbusters. The policy can be used to create more efficient online cultural markets with a better allocation of resources to products.},
  keywords =     {social-dynamics},
}

Unpredictability is often portrayed as an undesirable outcome of social in uence in cultural markets. Unpredictability stems from the ``rich get richer'' effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social in uence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of social influence. We investigate strategies for creating markets in which the popularity of products is better aligned with their underlying quality. For our study, we created a cultural market of science stories and conducted randomized experiments on different policies for presenting the stories to study participants. Specically, we varied how the stories were ranked, and whether or not participants were shown the ratings these stories received from others. We present a policy that leverages social influence and product positioning to help distinguish the product's market share (popularity) from underlying quality. Highlighting products with the highest estimated quality reduces the ``rich get richer'' effect of using popularity directly. We show that this policy allows us to more robustly and predictably identify high quality products and promote blockbusters. The policy can be used to create more efficient online cultural markets with a better allocation of resources to products.

@INPROCEEDINGS{Kooti2017wsdm,
  author =       {Farshad Kooti and Mihajlo Grbovic and Luca Maria Aiello and Eric Bax and Kristina Lerman},
  title =        {iPhone's Digital Marketplace: Characterizing the Big Spenders},
  booktitle =    {Proceedings of the 10th International ACM Conference on Web Search and Data Mining},
  year =         {2017},
  pages =        {},
  publisher =    {ACM},
  abstract =     {With mobile shopping surging in popularity, people are spending ever more money on digital purchases through their mobile devices and phones. However, few large-scale studies of mobile shopping exist. In this paper we analyze a large data set consisting of more than 776M digital purchases made on Apple mobile devices that include songs, apps, and in-app purchases. We find that 61\% of all the spending is on in-app purchases and that the top 1\% of users are responsible for 59\% of all the spending. These big spenders are more likely to be male and older, and less likely to be from the US. We study how they adopt and abandon individual app, and find that, after an initial phase of increased daily spending, users gradually lose interest: the delay between their purchases increases and the spending decreases with a sharp drop toward the end. Finally, we model the in-app purchasing behavior in multiple steps: 1) we model the time between purchases; 2) we train a classifier to predict whether the user will make a purchase from a new app or continue purchasing from the existing app; and 3) based on the outcome of the previous step, we attempt to predict the exact app, new or existing, from which the next purchase will come. The results yield},
  keywords =     {social-dynamics},
}

With mobile shopping surging in popularity, people are spending ever more money on digital purchases through their mobile devices and phones. However, few large-scale studies of mobile shopping exist. In this paper we analyze a large data set consisting of more than 776M digital purchases made on Apple mobile devices that include songs, apps, and in-app purchases. We find that 61% of all the spending is on in-app purchases and that the top 1% of users are responsible for 59% of all the spending. These big spenders are more likely to be male and older, and less likely to be from the US. We study how they adopt and abandon individual app, and find that, after an initial phase of increased daily spending, users gradually lose interest: the delay between their purchases increases and the spending decreases with a sharp drop toward the end. Finally, we model the in-app purchasing behavior in multiple steps: 1) we model the time between purchases; 2) we train a classifier to predict whether the user will make a purchase from a new app or continue purchasing from the existing app; and 3) based on the outcome of the previous step, we attempt to predict the exact app, new or existing, from which the next purchase will come. The results yield

@INPROCEEDINGS{KootiG2017www,
  author =       {Farshad Kooti and Mihajlo Grbovic and Luca Maria Aiello and Nemanja Djuric and Vladan Radosavljevic and Kristina Lerman},
  title =        {Analyzing the Ride-sharing Economy},
  booktitle =    {Proceedings of the 26th International World Wide Web Conference (Companion WWW2017)},
  year =         {2017},
  pages =        {},
  publisher =    {},
  abstract =     {Uber is a popular ride-sharing application that matches people who need a ride with others who are willing to provide it using their personal vehicles. Uber�s success has fueled the growth of the sharing economy, where consumers and providers exchange services in a peer-to-peer fashion. Despite its growing popularity, few largescale  studies examined Uber specifically, or the factors affecting user participation in the sharing economy in general. We address this gap through a large-scale study of the Uber market that analyzes 59M rides spanning a period of 7 months. These data were extracted from email receipts sent by Uber. Our data set allows us to examine the role of demographics, including age, gender, and race, on participation in the ride-sharing economy. The data is also fine-grained enough to evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches, e.g., riders to drivers of a similar age, can result in a higher driver ratings. Finally, we focus on factors that affect retention and use information from early rides to accurately predict which riders or drivers will become active Uber users.},
  keywords =     {social-dynamics},
}

Uber is a popular ride-sharing application that matches people who need a ride with others who are willing to provide it using their personal vehicles. Uber�s success has fueled the growth of the sharing economy, where consumers and providers exchange services in a peer-to-peer fashion. Despite its growing popularity, few largescale studies examined Uber specifically, or the factors affecting user participation in the sharing economy in general. We address this gap through a large-scale study of the Uber market that analyzes 59M rides spanning a period of 7 months. These data were extracted from email receipts sent by Uber. Our data set allows us to examine the role of demographics, including age, gender, and race, on participation in the ride-sharing economy. The data is also fine-grained enough to evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches, e.g., riders to drivers of a similar age, can result in a higher driver ratings. Finally, we focus on factors that affect retention and use information from early rides to accurately predict which riders or drivers will become active Uber users.

@INPROCEEDINGS{KootiA2017www,
  author =       {Farshad Kooti and Karthik Subbian and Winter Mason and Lada Adamic and Kristina Lerman},
  title =        {Understanding Short-term Changes in Online Activity Sessions},
  booktitle =    {Proceedings of the 26th International World Wide Web Conference (Companion WWW2017)},
  year =         {2017},
  pages =        {},
  publisher =    {},
  abstract =     {Online activity is characterized by regularities such as diurnal and weekly patterns, reflecting human circadian rhythms and work and leisure schedules. Using data from the online social networking site Facebook, we uncover temporal patterns at a much smaller time scale: within individual sessions. Longer sessions have different characteristics than shorter ones, and this distinction is already visible in the first minute of a person�s session activity. This allows us to predict the ultimate length of his or her session and how much content the person will see. The length of the session and other factors are in turn predictive of when the individual will return. Within a session, the amount of time a person spends on different kinds of content depends on both the person�s demographic attributes, such as age and the number of Facebook friends, and the length of the time elapsed since the start of the session. We also find that liking and commenting is very non-uniformly distributed between sessions. Predictions of session duration and activity can potentially be leveraged to more efficiently cache content, especially to mobile devices in places with poor communications infrastructure, in order to improve user online experience.},
  keywords =     {social-dynamics, cognitive-depletion},
}

Online activity is characterized by regularities such as diurnal and weekly patterns, reflecting human circadian rhythms and work and leisure schedules. Using data from the online social networking site Facebook, we uncover temporal patterns at a much smaller time scale: within individual sessions. Longer sessions have different characteristics than shorter ones, and this distinction is already visible in the first minute of a person�s session activity. This allows us to predict the ultimate length of his or her session and how much content the person will see. The length of the session and other factors are in turn predictive of when the individual will return. Within a session, the amount of time a person spends on different kinds of content depends on both the person�s demographic attributes, such as age and the number of Facebook friends, and the length of the time elapsed since the start of the session. We also find that liking and commenting is very non-uniformly distributed between sessions. Predictions of session duration and activity can potentially be leveraged to more efficiently cache content, especially to mobile devices in places with poor communications infrastructure, in order to improve user online experience.

@article{lerman2017bounded,
  title={Bounded rationality in scholarly knowledge discovery},
  author={Lerman, Kristina and Hodas, Nathan and Wu, Hao},
  journal={arXiv preprint arXiv:1710.00269},
  year={2017}
}

@INPROCEEDINGS{Kooti2016socinfo,
  author =       {Farshad Kooti and Esteban Moro and Kristina Lerman},
  title =        {Twitter Session Analytics: Profiling Users� Short-term Behavioral Changes},
  booktitle =    {Proceedings of the 8th International Conference (SocInfo2016)},
  year =         {2016},
  editor =       {E Spiro and YY Ahn},
  pages =        {71--86},
  publisher =    {Springer},
  keywords =     {social-dynamics, cognitive-depletion},
  abstract={Human behavior shows strong daily, weekly, and monthly patterns. In this work, we demonstrate online behavioral changes that occur on a much smaller time scale: minutes, rather than days or weeks. Specifically, we study how people distribute their effort over different tasks during periods of activity on the Twitter social platform. We demonstrate that later in a session on Twitter, people prefer to perform simpler tasks, such as replying and retweeting others� posts, rather than composing original messages, and they also tend to post shorter messages. We measure the strength of this effect empirically and statistically using mixed-effects models, and find that the first post of a session is up to 25\% more likely to be a composed message, and 10- 20\% less likely to be a reply or retweet. Qualitatively, our results hold for different populations of Twitter users segmented by how active and well-connected they are. Although our work does not resolve the mechanisms responsible for these behavioral changes, our results offer insights for improving user experience and engagement on online social platforms.}
}

Human behavior shows strong daily, weekly, and monthly patterns. In this work, we demonstrate online behavioral changes that occur on a much smaller time scale: minutes, rather than days or weeks. Specifically, we study how people distribute their effort over different tasks during periods of activity on the Twitter social platform. We demonstrate that later in a session on Twitter, people prefer to perform simpler tasks, such as replying and retweeting others� posts, rather than composing original messages, and they also tend to post shorter messages. We measure the strength of this effect empirically and statistically using mixed-effects models, and find that the first post of a session is up to 25% more likely to be a composed message, and 10- 20% less likely to be a reply or retweet. Qualitatively, our results hold for different populations of Twitter users segmented by how active and well-connected they are. Although our work does not resolve the mechanisms responsible for these behavioral changes, our results offer insights for improving user experience and engagement on online social platforms.

@article{Smith2016tkdd,
 author = {Smith, Laura M. and Zhu, Linhong and Lerman, Kristina and Percus, Allon G.},
 title = {Partitioning Networks with Node Attributes by Compressing Information Flow},
 journal = {ACM Trans. Knowl. Discov. Data},
     abstract = {Real-world networks are often organized as modules or communities of similar
nodes that serve as functional units. These networks are also rich in content,
with nodes having distinguishing features or attributes. In order to discover a
network's modular structure, it is necessary to take into account not only its
links but also node attributes. We describe an information-theoretic method
that identifies modules by compressing descriptions of information flow on a
network. Our formulation introduces node content into the description of
information flow, which we then minimize to discover groups of nodes with
similar attributes that also tend to trap the flow of information. The method
has several advantages: it is conceptually simple and does not require ad-hoc
parameters to specify the number of modules or to control the relative
contribution of links and node attributes to network structure. We apply the
proposed method to partition real-world networks with known community
structure. We demonstrate that adding node attributes helps recover the
underlying community structure in content-rich networks more effectively than
using links alone. In addition, we show that our method is faster and more
accurate than alternative state-of-the-art algorithms.},
 issue_date = {November 2016},
 volume = {11},
 number = {2},
 month = nov,
 year = {2016},
 issn = {1556-4681},
 pages = {15:1--15:26},
 articleno = {15},
 numpages = {26},
    url = {http://arxiv.org/abs/1405.4332},
 doi = {10.1145/2968451},
 acmid = {2968451},
 publisher = {ACM},
 address = {New York, NY, USA},
     keywords = {social-networks},
}

Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguishing features or attributes. In order to discover a network's modular structure, it is necessary to take into account not only its links but also node attributes. We describe an information-theoretic method that identifies modules by compressing descriptions of information flow on a network. Our formulation introduces node content into the description of information flow, which we then minimize to discover groups of nodes with similar attributes that also tend to trap the flow of information. The method has several advantages: it is conceptually simple and does not require ad-hoc parameters to specify the number of modules or to control the relative contribution of links and node attributes to network structure. We apply the proposed method to partition real-world networks with known community structure. We demonstrate that adding node attributes helps recover the underlying community structure in content-rich networks more effectively than using links alone. In addition, we show that our method is faster and more accurate than alternative state-of-the-art algorithms.

@ARTICLE{Merkurjev2016ip,
  author =       {Ekaterina Merkurjev and Andrea Bertozzi and Xiaoran Yan and Kristina Lerman},
  title =        {Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data},
  journal =      {to appear in Inverse Problems},
  year =         {2016},
  volume =       {},
  number =       {},
  pages =        {},
  abstract={Recent advances in clustering have included continuous relaxations of the Cheeger cut problem and those which address its linear approximation using the graph Laplacian. In this paper, we show how to use the graph Laplacian to solve the fully nonlinear Cheeger cut problem, as well as the ratio cut optimization task. Both problems are connected to total variation minimization, and the related Ginzburg-Landau functional is used in the derivation of the methods. The graph framework discussed in this paper is undirected. The resulting algorithms are efficient ways to cluster the data into two classes, and they can be easily extended to the case of multiple classes, or used on a multiclass data set via recursive bipartitioning. In addition to showing results on benchmark data sets, we also show an application of the algorithm to hyperspectral video data.},
  keywords =     {social-networks},
}

Recent advances in clustering have included continuous relaxations of the Cheeger cut problem and those which address its linear approximation using the graph Laplacian. In this paper, we show how to use the graph Laplacian to solve the fully nonlinear Cheeger cut problem, as well as the ratio cut optimization task. Both problems are connected to total variation minimization, and the related Ginzburg-Landau functional is used in the derivation of the methods. The graph framework discussed in this paper is undirected. The resulting algorithms are efficient ways to cluster the data into two classes, and they can be easily extended to the case of multiple classes, or used on a multiclass data set via recursive bipartitioning. In addition to showing results on benchmark data sets, we also show an application of the algorithm to hyperspectral video data.

@INPROCEEDINGS{Hogg2016hcomp,
  author =       {Tad Hogg and Kristina Lerman},
  title =        {Leveraging the Contributions of the Casual Majority to Identify AppealingWeb
Content},
  booktitle =    {Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2016)},
  year =         {2016},
  month =        {November},
  publisher =    {AAAI},
  keywords =     {crowdsourcing},
  abstract={Users of peer production web sites differ greatly in their activity levels. A small minority are engaged contributors, while the vast majority are only casual surfers. The casual users devote little effort to evaluating the site�s content and many of them visit the site only once. This churn poses a challenge for sites attempting to gauge user interest in their content. The challenge is especially severe for sites focusing on content with subjective quality, including movies, music, restaurants and items in other cultural markets. A key question is whether content evaluation should use opinions of all users or only the minority who devote significant effort to reviewing content? Using Amazon Mechanical Turk, we experimentally address this question by comparing outcomes for these two approaches. We find that the larger numbers of less informed users more than offset their noisy signals on content quality to provide rapid evaluation. However, such users are systematically biased, and the speed of their assessments comes at the expense of limited collective accuracy.},
  url={http://www.isi.edu/integration/people/lerman/papers/hogg-hcomp.pdf}
}

Users of peer production web sites differ greatly in their activity levels. A small minority are engaged contributors, while the vast majority are only casual surfers. The casual users devote little effort to evaluating the site�s content and many of them visit the site only once. This churn poses a challenge for sites attempting to gauge user interest in their content. The challenge is especially severe for sites focusing on content with subjective quality, including movies, music, restaurants and items in other cultural markets. A key question is whether content evaluation should use opinions of all users or only the minority who devote significant effort to reviewing content? Using Amazon Mechanical Turk, we experimentally address this question by comparing outcomes for these two approaches. We find that the larger numbers of less informed users more than offset their noisy signals on content quality to provide rapid evaluation. However, such users are systematically biased, and the speed of their assessments comes at the expense of limited collective accuracy.

@article{Yan2016peerj,
    abstract = {We study the interplay between a dynamical process and the structure of the network on which it unfolds using the parameterized Laplacian framework. This framework allows for defining and characterizing an ensemble of dynamical processes on a network beyond what the traditional Laplacian is capable of modeling. This, in turn, allows for studying the impact of the interaction between dynamics and network topology on the quality-measure of network clusters and centrality, in order to effectively identify important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that captures a vertex's participation in the dynamical process on a given network and also define a function that measures the quality of every subset of vertices as a potential cluster (or community) with respect to this process. We show that the subset-quality function generalizes the traditional conductance measure for graph partitioning. We partially justify our choice of the quality function by showing that the classic Cheeger's inequality, which relates the conductance of the best cluster in a network with a spectral quantity of its Laplacian matrix, can be extended to the parameterized Laplacian. The parameterized Laplacian framework brings under the same umbrella a surprising variety of dynamical processes and allows us to systematically compare the different perspectives they create on network structure.},
    author = {Yan, Xiaoran and Teng, Shanghua and Ghosh, Rumi and Lerman, Kristina},
    doi = {https://doi.org/10.7717/peerj-cs.57},
    journal = {PeerJ Computer Science},
    pages = {e57+},
    title = {Capturing the interplay of dynamics and networks through parameterizations of Laplacian operators.},
    url = {https://peerj.com/articles/cs-57/},
    volume = {2},
    keywords={social-networks, social-dynamics},
    year = {2016}
}

We study the interplay between a dynamical process and the structure of the network on which it unfolds using the parameterized Laplacian framework. This framework allows for defining and characterizing an ensemble of dynamical processes on a network beyond what the traditional Laplacian is capable of modeling. This, in turn, allows for studying the impact of the interaction between dynamics and network topology on the quality-measure of network clusters and centrality, in order to effectively identify important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that captures a vertex's participation in the dynamical process on a given network and also define a function that measures the quality of every subset of vertices as a potential cluster (or community) with respect to this process. We show that the subset-quality function generalizes the traditional conductance measure for graph partitioning. We partially justify our choice of the quality function by showing that the classic Cheeger's inequality, which relates the conductance of the best cluster in a network with a spectral quantity of its Laplacian matrix, can be extended to the parameterized Laplacian. The parameterized Laplacian framework brings under the same umbrella a surprising variety of dynamical processes and allows us to systematically compare the different perspectives they create on network structure.

@article{Lerman2016futureinternet,
    abstract = {The many decisions that people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.},
    author = {Lerman, Kristina},
    doi = {10.3390/fi8020021},
    journal = {Future Internet},
    keywords = {cognitive-limits, social-networks},
    month = may,
    number = {2},
    pages = {21+},
    title = {Information Is Not a Virus, and Other Consequences of Human Cognitive Limits},
    url = {http://dx.doi.org/10.3390/fi8020021},
    volume = {8},
    year = {2016}
}

The many decisions that people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.

@article{Lamprecht2016,
    abstract = {In this work we study how people navigate the information network of Wikipedia and investigate (i) free-form navigation by studying all clicks within the English Wikipedia over an entire month and (ii) goal-directed Wikipedia navigation by analyzing wikigames, where users are challenged to retrieve articles by following links. To study how the organization of Wikipedia articles in terms of layout and links affects navigation behavior, we first investigate the characteristics of the structural organization and of hyperlinks in Wikipedia and then evaluate link selection models based on article structure and other potential influences in navigation, such as the generality of an article's topic. In free-form Wikipedia navigation, covering all Wikipedia usage scenarios, we find that click choices can be best modeled by a bias towards article structure, such as a tendency to click links located in the lead section. For the goal-directed navigation of wikigames, our findings confirm the zoom-out and the homing-in phases identified by previous work, where users are guided by generality at first and textual similarity to the target later. However, our interpretation of the link selection models accentuates that article structure is the best explanation for the navigation paths in all except these initial and final stages. Overall, we find evidence that users more frequently click on links that are located close to the top of an article. The structure of Wikipedia articles, which places links to more general concepts near the top, supports navigation by allowing users to quickly find the better-connected articles that facilitate navigation. Our results highlight the importance of article structure and link position in Wikipedia navigation and suggest that better organization of information can help make information networks more navigable.},
    author = {Lamprecht, Daniel and Lerman, Kristina and Helic, Denis and Strohmaier, Markus},
    doi = {10.1080/13614568.2016.1179798},
    journal = {New Review of Hypermedia and Multimedia},
    keywords = {cognitive-limits},
    month = may,
    pages = {1--22},
    publisher = {Taylor \& Francis},
    title = {How the structure of Wikipedia articles influences user navigation},
    url = {http://dx.doi.org/10.1080/13614568.2016.1179798},
    year = {2016}
}

In this work we study how people navigate the information network of Wikipedia and investigate (i) free-form navigation by studying all clicks within the English Wikipedia over an entire month and (ii) goal-directed Wikipedia navigation by analyzing wikigames, where users are challenged to retrieve articles by following links. To study how the organization of Wikipedia articles in terms of layout and links affects navigation behavior, we first investigate the characteristics of the structural organization and of hyperlinks in Wikipedia and then evaluate link selection models based on article structure and other potential influences in navigation, such as the generality of an article's topic. In free-form Wikipedia navigation, covering all Wikipedia usage scenarios, we find that click choices can be best modeled by a bias towards article structure, such as a tendency to click links located in the lead section. For the goal-directed navigation of wikigames, our findings confirm the zoom-out and the homing-in phases identified by previous work, where users are guided by generality at first and textual similarity to the target later. However, our interpretation of the link selection models accentuates that article structure is the best explanation for the navigation paths in all except these initial and final stages. Overall, we find evidence that users more frequently click on links that are located close to the top of an article. The structure of Wikipedia articles, which places links to more general concepts near the top, supports navigation by allowing users to quickly find the better-connected articles that facilitate navigation. Our results highlight the importance of article structure and link position in Wikipedia navigation and suggest that better organization of information can help make information networks more navigable.

@article{Singer2016plosone,
    abstract = {This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.},
    author = {Singer, Philipp and Ferrara, Emilio and Kooti, Farshad and Strohmaier, Markus and Lerman, Kristina},
    doi = {10.1371/journal.pone.0161636},
    journal = {PLoS ONE},
    keywords = {cognitive-depletion, lerman, reddit},
    number = {8},
    pages = {e0161636+},
    publisher = {Public Library of Science},
    title = {Evidence of Online Performance Deterioration in User Sessions on Reddit},
    url = {http://dx.doi.org/10.1371/journal.pone.0161636},
    volume = {11},
    year = {2016}
}

This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.

@inproceedings{Geigl16hypertext,
    abstract = {Websites have an inherent interest in steering user navigation in order to,
for example, increase sales of specific products or categories, or to guide
users towards specific information. In general, website administrators can use
the following two strategies to influence their visitors' navigation behavior.
First, they can introduce click biases to reinforce specific links on their
website by changing their visual appearance, for example, by locating them on
the top of the page. Second, they can utilize link insertion to generate new
paths for users to navigate over. In this paper, we present a novel approach
for measuring the potential effects of these two strategies on user navigation.
Our results suggest that, depending on the pages for which we want to increase
user visits, optimal link modification strategies vary. Moreover, simple
topological measures can be used as proxies for assessing the impact of the
intended changes on the navigation of users, even before these changes are
implemented.},
     booktitle =    {Hypertext Conference},
    author = {Geigl, Florian and Lerman, Kristina and Walk, Simon and Strohmaier, Markus and Helic, Denis},
    title = {Assessing the Navigational Effects of Click Biases and Link Insertion on the Web},
    url = {http://arxiv.org/abs/1603.06200},
    year = {2016}
}

Websites have an inherent interest in steering user navigation in order to, for example, increase sales of specific products or categories, or to guide users towards specific information. In general, website administrators can use the following two strategies to influence their visitors' navigation behavior. First, they can introduce click biases to reinforce specific links on their website by changing their visual appearance, for example, by locating them on the top of the page. Second, they can utilize link insertion to generate new paths for users to navigate over. In this paper, we present a novel approach for measuring the potential effects of these two strategies on user navigation. Our results suggest that, depending on the pages for which we want to increase user visits, optimal link modification strategies vary. Moreover, simple topological measures can be used as proxies for assessing the impact of the intended changes on the navigation of users, even before these changes are implemented.

@INPROCEEDINGS{Lerman2016icwsm,
  author =       {Kristina Lerman and Megha Arora and Luciano Gallegos and Ponnurangam Kumaraguru and David Garcia},
  title =        {Emotions, Demographics and Sociability in Twitter Interactions},
  booktitle =    {International Conference on the Web and Social Media},
  year =         {2016},
  keywords =     {social-psychology},
  abstract = {The social connections people form online affect the quality of information they receive and their online experience. Although a host of socioeconomic and cognitive factors were implicated in the formation of offline social ties, few of them have been empirically validated, particularly in an online setting. In this study, we analyze a large corpus of geo-referenced messages, or tweets, posted by social media users from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in the tweets posted from an area, the structure of social connections, and also use that area's socioeconomic characteristics in analysis.  %We extracted the structure of online social interactions from the people mentioned in tweets from that area.
We find that at an aggregate level, places where social media users engage more deeply with less diverse social contacts are those where they express more negative emotions, like sadness and anger. Demographics also has an impact: these places have residents with lower household income and education levels. Conversely, places where people engage less frequently but with diverse contacts have happier, more positive messages posted from them and also have better educated, younger, more affluent residents. Results suggest that cognitive factors and offline characteristics affect the quality of online interactions. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.
},
url={http://arxiv.org/abs/1510.07090},
}

The social connections people form online affect the quality of information they receive and their online experience. Although a host of socioeconomic and cognitive factors were implicated in the formation of offline social ties, few of them have been empirically validated, particularly in an online setting. In this study, we analyze a large corpus of geo-referenced messages, or tweets, posted by social media users from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in the tweets posted from an area, the structure of social connections, and also use that area's socioeconomic characteristics in analysis. %We extracted the structure of online social interactions from the people mentioned in tweets from that area. We find that at an aggregate level, places where social media users engage more deeply with less diverse social contacts are those where they express more negative emotions, like sadness and anger. Demographics also has an impact: these places have residents with lower household income and education levels. Conversely, places where people engage less frequently but with diverse contacts have happier, more positive messages posted from them and also have better educated, younger, more affluent residents. Results suggest that cognitive factors and offline characteristics affect the quality of online interactions. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.

@article{Lerman2016majority,
    author = {Kristina Lerman and Xiaoran Yan and Xin-Zeng Wu},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {The "Majority Illusion" in Social Networks},
    year = {2016},
    month = {02},
    volume = {11},
      keywords =     {social-networks},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0147617},
    pages = {1-13},
    abstract = {Individual's decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual?s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the ?majority illusion,? leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.</p>},
    number = {2},
}

Individual's decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual?s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the ?majority illusion,? leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

@INPROCEEDINGS{Gallegos2016msm,
  author =       {Luciano Gallegos and Kristina Lerman and Arthur Huang and David Garcia},
  title =        {Geography of Emotion: Where in a City are People Happier?},
  booktitle =    {WWW workshop on MSM},
  year =         {2016},
  keywords =     {social-dynamics},
  abstract = {During the last years, researchers explored the geographic and environmental factors that affect happiness. More recently,
location-sharing services provided by the social media has given an unprecedented access to geo-located data
for studying the interplay between these factors on a much bigger scale. Do location-sharing services help in turn at
distinguishing emotions in places within a city? Which aspects contribute better at understanding happier places?
To answer these questions, we use data from Foursquare location-sharing service to identify areas within a major US
metropolitan area with many check-ins, i.e., areas that people like to use. We then use data from the Twitter microblogging
platform to analyze the properties of these areas. Specifically, we have extracted a large corpus of geotagged
messages, called tweets, from a major metropolitan area and linked them US Census data through their locations.
This allows us to measure the sentiment expressed in tweets that are posted from a specific area, and also use that
area's demographic properties in analysis. Our results reveal that areas with many check-ins are diffierent from other
areas within the metropolitan region. In particular, these areas have happier tweets, which also encourage people living in it or from other areas to commute longer distances
to these places. These findings shed light on the influence certain places play within a city regarding people's emotions
and mobility, which in turn can be used for city planners for designing happier and more equitable cities.},
url={http://arxiv.org/abs/1507.07632},
}

During the last years, researchers explored the geographic and environmental factors that affect happiness. More recently, location-sharing services provided by the social media has given an unprecedented access to geo-located data for studying the interplay between these factors on a much bigger scale. Do location-sharing services help in turn at distinguishing emotions in places within a city? Which aspects contribute better at understanding happier places? To answer these questions, we use data from Foursquare location-sharing service to identify areas within a major US metropolitan area with many check-ins, i.e., areas that people like to use. We then use data from the Twitter microblogging platform to analyze the properties of these areas. Specifically, we have extracted a large corpus of geotagged messages, called tweets, from a major metropolitan area and linked them US Census data through their locations. This allows us to measure the sentiment expressed in tweets that are posted from a specific area, and also use that area's demographic properties in analysis. Our results reveal that areas with many check-ins are diffierent from other areas within the metropolitan region. In particular, these areas have happier tweets, which also encourage people living in it or from other areas to commute longer distances to these places. These findings shed light on the influence certain places play within a city regarding people's emotions and mobility, which in turn can be used for city planners for designing happier and more equitable cities.

@inproceedings{Huang2016,
  title={How Business Clusters and Destination Choice are Connected: A Model Based on Social Media Data},
  author={Huang, Arthur and Gallegos, Luciano and Lerman, Kristina},
  booktitle={Proceedings of the 6th TRB Innovations in Travel Modeling Conference},
  year={2016},
  organization={Transportation Research Board}
}

@article{darpabotchallenge,
    abstract = {A number of organizations ranging from terrorist groups such as {ISIS} to
politicians and nation states reportedly conduct explicit campaigns to
influence opinion on social media, posing a risk to democratic processes. There
is thus a growing need to identify and eliminate "influence bots" - realistic,
automated identities that illicitly shape discussion on sites like Twitter and
Facebook - before they get too influential. Spurred by such events, {DARPA} held
a 4-week competition in {February/March} 2015 in which multiple teams supported
by the {DARPA} Social Media in Strategic Communications program competed to
identify a set of previously identified "influence bots" serving as ground
truth on a specific topic within Twitter. Past work regarding influence bots
often has difficulty supporting claims about accuracy, since there is limited
ground truth (though some exceptions do exist [3,7]). However, with the
exception of [3], no past work has looked specifically at identifying influence
bots on a specific topic. This paper describes the {DARPA} Challenge and
describes the methods used by the three top-ranked teams.},
    author = {Subrahmanian, V. S. and Azaria, Amos and Durst, Skylar and Kagan, Vadim and Galstyan, Aram and Lerman, Kristina and Zhu, Linhong and Ferrara, Emilio and Flammini, Alessandro and Menczer, Filippo and Waltzman, Rand and Stevens, Andrew and Dekhtyar, Alexander and Gao, Shuyang and Hogg, Tad and Kooti, Farshad and Liu, Yan and Varol, Onur and Shiralkar, Prashant and Vydiswaran, Vinod and Mei, Qiaozhu and Huang, Tim},
    journal = {IEEE Computer Magazine},
    title = {The {DARPA} Twitter Bot Challenge},
    url = {http://arxiv.org/abs/1601.05140},
    year = {2016}
}

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.

@INPROCEEDINGS{Kooti16wsdm,
  author =       {Farshad Kooti and Kristina Lerman and Luca Maria Aiello and Mihajlo Grbovic and Nemanja Djuric and Vladan Radosavljevic},
  title =        {Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior},
  booktitle =    {The 9th ACM International Conference on Web Search and Data Mining},
  year =         {2016},
  keywords =     {social-dynamics},
  abstract = {Consumer spending accounts for a large fraction of US economic
activity. Increasingly, consumer activity is moving online, where
digital traces of shopping and purchases provide valuable data about
consumer behavior. We analyze these data extracted from emails
and combine them with demographic information to characterize,
model, and predict consumer behavior. Breaking purchasing down
by age and gender, we find that the amount of money spent on online
purchases grows sharply with age, peaking in late 30s. Men
are more frequent online purchasers and spend more money compared
to women. Linking online shopping to income, we find that
shoppers from more affluent areas purchase somewhat more expensive
items and buy them more frequently, resulting in significantly
more money spent on online purchases. We also look at dynamics
of purchasing behavior. Similar to other online activities, we
observe daily and weekly cycles in purchasing behavior. More interestingly,
we observe temporal patterns in individual purchasing
behavior suggesting shoppers have finite budgets: the more expensive
an item, the longer the shopper waits since the last purchase
to buy it. We also observe that shoppers who email each other
purchase more similar items than socially unconnected shoppers,
and this effect is particularly evident among women. Finally, we
build a model to predict when shoppers will make a purchase and
how much they will spend on it. We find that temporal features
improve prediction accuracy over competitive baselines. A better
understanding of consumer behavior can help improve marketing
efforts and make online shopping more pleasant and efficient.},
url={http://arxiv.org/abs/1512.04912},
}

Consumer spending accounts for a large fraction of US economic activity. Increasingly, consumer activity is moving online, where digital traces of shopping and purchases provide valuable data about consumer behavior. We analyze these data extracted from emails and combine them with demographic information to characterize, model, and predict consumer behavior. Breaking purchasing down by age and gender, we find that the amount of money spent on online purchases grows sharply with age, peaking in late 30s. Men are more frequent online purchasers and spend more money compared to women. Linking online shopping to income, we find that shoppers from more affluent areas purchase somewhat more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases. We also look at dynamics of purchasing behavior. Similar to other online activities, we observe daily and weekly cycles in purchasing behavior. More interestingly, we observe temporal patterns in individual purchasing behavior suggesting shoppers have finite budgets: the more expensive an item, the longer the shopper waits since the last purchase to buy it. We also observe that shoppers who email each other purchase more similar items than socially unconnected shoppers, and this effect is particularly evident among women. Finally, we build a model to predict when shoppers will make a purchase and how much they will spend on it. We find that temporal features improve prediction accuracy over competitive baselines. A better understanding of consumer behavior can help improve marketing efforts and make online shopping more pleasant and efficient.

@ARTICLE{Hogg2015hcomp,
  author =       {Tad Hogg and Kristina Lerman},
  title =        {Disentangling the Effects of Social Signals},
  journal =      {Human Computation Journal},
  year =         {2015},
  volume =       {2},
  number =       {2},
  pages =        {189--208},
  abstract =     {Peer recommendation is a crowdsourcing task that leverages the opinions of
  many to identify interesting content online, such as news, images, or videos. Peer
  recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social  signals in peer recommendation. Specifically, after controlling for variation due to item content  and its position, we find that social  signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social  signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social  signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.
},
 url={http://arxiv.org/abs/1410.6744}
}

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the ``rich get richer'' phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a ``herding'' effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.

@UNPUBLISHED{Lerman15qss,
  author =       {Kristina Lerman and Nathan O. Hodas and Hao Wu},
  title =        {Bounded Rationality in Scientific Knowledge Discovery},
  note =         {presented at Quantifying Success in Science workshop at CSS},
  year =         {2015},
}

@INPROCEEDINGS{Kang15icwsm,
  AUTHOR =       {Jeonhyung Kang and Kristina Lerman},
  TITLE =        {User Effort and Network Structure Mediate Access to Information in Networks},
  BOOKTITLE =    {Proceedings of the 9th  International AAAI Conference on Weblogs and Social Media (ICWSM)},
  YEAR =         {2015},
  month =        {},
  url = {http://arxiv.org/abs/1504.01760},
  keywords =     {social-networks},
  abstract = {Individuals' access to information in a social network depends on its distributed and where in the network individuals position themselves. However, individuals have limited capacity to manage their social connections and process information. In this work, we study how this limited capacity and network structure interact to affect the diversity of information social media users receive. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users by observing messages they share online with their followers. We present a probabilistic model that incorporates human cognitive constraints in a generative model of information sharing. We then use the topics learned by the model to measure the diversity of information users receive from their social media contacts. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower graph, are exposed to more diverse information. In addition, we identify user effort as an important variable that mediates access to diverse information in social media. Users who invest more effort into their activity on the site not only place themselves in more structurally diverse positions within the network than the less engaged users, but they also receive more diverse information when located in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.},
}

Individuals' access to information in a social network depends on its distributed and where in the network individuals position themselves. However, individuals have limited capacity to manage their social connections and process information. In this work, we study how this limited capacity and network structure interact to affect the diversity of information social media users receive. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users by observing messages they share online with their followers. We present a probabilistic model that incorporates human cognitive constraints in a generative model of information sharing. We then use the topics learned by the model to measure the diversity of information users receive from their social media contacts. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower graph, are exposed to more diverse information. In addition, we identify user effort as an important variable that mediates access to diverse information in social media. Users who invest more effort into their activity on the site not only place themselves in more structurally diverse positions within the network than the less engaged users, but they also receive more diverse information when located in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.

@INPROCEEDINGS{Kooti15www,
  AUTHOR =       {Farshad Kooti and Luca Maria Aiello and Mihajlo Grbovic and Kristina Lerman and Amin Mantrach},
  TITLE =        {Evolution of Conversations in the Age of Email Overload},
  BOOKTITLE =    {Proceedings of 24th International World Wide Web Conferenced (WWW)},
  YEAR =         {2015},
  month =        {May},
  url = {http://arxiv.org/abs/1504.00704},
  keywords =     {social-dynamics},
  abstract={Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.},
}

Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.

@INPROCEEDINGS{Kang15sbp,
  AUTHOR =       {Jeon-hyung Kang and Kristina Lerman},
  TITLE =        {VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting},
  BOOKTITLE =    {International Conference on Social Computing, Behavioral Modeling and Prediction},
  YEAR =         {2015},
  month =        {April},
  url = {http://arxiv.org/abs/1502.00582},
  keywords =     {social-networks},
  abstract = {Information spread in social media depends on a number of factors, including how the site displays information, how users navigate it to find items of interest, users' tastes, and the `virality' of information, i.e., its propensity to be adopted, or retweeted, upon exposure. Probabilistic models can learn users' tastes from the history of their item adoptions and recommend new items to users. However, current models ignore cognitive biases that are known to affect behavior. Specifically, people pay more attention to items at the top of a list than those in lower positions. As a consequence, items near the top of a user's social media stream have higher visibility, and are more likely to be seen and adopted, than those appearing below. Another bias is due to the item's fitness: some items have a high propensity to spread upon exposure regardless of the interests of adopting users. We propose a probabilistic model that incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.},
}

Information spread in social media depends on a number of factors, including how the site displays information, how users navigate it to find items of interest, users' tastes, and the `virality' of information, i.e., its propensity to be adopted, or retweeted, upon exposure. Probabilistic models can learn users' tastes from the history of their item adoptions and recommend new items to users. However, current models ignore cognitive biases that are known to affect behavior. Specifically, people pay more attention to items at the top of a list than those in lower positions. As a consequence, items near the top of a user's social media stream have higher visibility, and are more likely to be seen and adopted, than those appearing below. Another bias is due to the item's fitness: some items have a high propensity to spread upon exposure regardless of the interests of adopting users. We propose a probabilistic model that incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.

@INPROCEEDINGS{Gupta15sbp,
  AUTHOR =       {Sidharth Gupta and Xiaoran Yan and Kristina Lerman},
  TITLE =        {Structural Properties of Ego Networks},
  BOOKTITLE =    {International Conference on Social Computing, Behavioral Modeling and Prediction},
  YEAR =         {2015},
  month =        {April},
  url = {http://arxiv.org/abs/1411.6061},
  keywords =     {social-networks},
  abstract={The structure of real-world social networks in large part determines the evolution of social phenomena, including opinion formation, diffusion of information and influence, and the spread of disease. Globally, network structure is characterized by features such as degree distribution, degree assortativity, and clustering coefficient. However, information about global structure is usually not available to each vertex. Instead, each vertex's knowledge is generally limited to the locally observable portion of the network consisting of the subgraph over its immediate neighbors. Such subgraphs, known as ego networks, have properties that can differ substantially from those of the global network. In this paper, we study the structural properties of ego networks and show how they relate to the global properties of networks from which they are derived. Through empirical comparisons and mathematical derivations, we show that structural features, similar to static attributes, suffer from paradoxes. We quantify the differences between global information about network structure and local estimates. This knowledge allows us to better identify and correct the biases arising from incomplete local information.},
}

The structure of real-world social networks in large part determines the evolution of social phenomena, including opinion formation, diffusion of information and influence, and the spread of disease. Globally, network structure is characterized by features such as degree distribution, degree assortativity, and clustering coefficient. However, information about global structure is usually not available to each vertex. Instead, each vertex's knowledge is generally limited to the locally observable portion of the network consisting of the subgraph over its immediate neighbors. Such subgraphs, known as ego networks, have properties that can differ substantially from those of the global network. In this paper, we study the structural properties of ego networks and show how they relate to the global properties of networks from which they are derived. Through empirical comparisons and mathematical derivations, we show that structural features, similar to static attributes, suffer from paradoxes. We quantify the differences between global information about network structure and local estimates. This knowledge allows us to better identify and correct the biases arising from incomplete local information.

@ARTICLE{Ghosh15gmas,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {The Impact of Network Flows on Community Formation in Models of Opinion Dynamics},
  JOURNAL =      {Journal of Mathematical Sociology},
  YEAR =         {2015},
  volume =       {39},
  number =       {},
  pages =        {109--124},
  month =        {},
  note =         {},
  keywords =     {social-networks},
  url={http://www.tandfonline.com/doi/pdf/10.1080/0022250X.2014.905776},
  abstract =     {We study dynamics of opinion formation in a network of coupled agents. As the network
evolves to a steady state, opinions of agents within the same community converge faster than
those of other agents. This framework allows us to study how network topology and
network flow, which mediates the transfer of opinions between agents, both affect the
formation of communities. In traditional models of opinion dynamics, agents are coupled
via conservative flows, which result in one-to-one opinion transfer. However, social
interactions are often nonconservative, resulting in one-to-many transfer of opinions.
We study opinion formation in networks using one-to-one and one-to-many interactions
and show that they lead to different community structure within the same network.}
  }

We study dynamics of opinion formation in a network of coupled agents. As the network evolves to a steady state, opinions of agents within the same community converge faster than those of other agents. This framework allows us to study how network topology and network flow, which mediates the transfer of opinions between agents, both affect the formation of communities. In traditional models of opinion dynamics, agents are coupled via conservative flows, which result in one-to-one opinion transfer. However, social interactions are often nonconservative, resulting in one-to-many transfer of opinions. We study opinion formation in networks using one-to-one and one-to-many interactions and show that they lead to different community structure within the same network.

@INPROCEEDINGS{Intagorn14acmgis,
  AUTHOR =       {Suradej Intagorn and Kristina Lerman},
  TITLE =        {Placing User-generated Content on the Map with Confidence},
  BOOKTITLE =    {ACM GIS},
  YEAR =         {2014},
  abstract =     {We describe a method that predicts the location of user-generated
content using textual features alone. Unlike previous methods for
geotagging text documents, our proposed method is not sensitive
to how we discretize space. We also discover that spatial resolu-
tion has an impact on the prediction accuracy, which allows us to
trade-off the spatial resolution of the predicted location against our
confidence about its accuracy. Our method can be used to estimate
the error in document�s predicted location, enabling us to filter out
poor quality predictions. We evaluate the proposed method exten-
sively on user-generated content collected from two different social
media sites, Flickr and Twitter. Our evaluation examines its perfor-
mance on the geotagging task and with respect to different parame-
ters. We achieve state-of-the-art results for all three tasks: location
prediction, error estimation and result ranking and also provide a
theoretical explanation of the effect of spatial resolution factor on
geotagging accuracy. Our findings provide valuable insights into
the design of geotagging systems and their quality control.},
  keywords =     {social-annotation},
  url={http://www.isi.edu/integration/people/lerman/papers/Intagorn14acmgis.pdf}
}

We describe a method that predicts the location of user-generated content using textual features alone. Unlike previous methods for geotagging text documents, our proposed method is not sensitive to how we discretize space. We also discover that spatial resolu- tion has an impact on the prediction accuracy, which allows us to trade-off the spatial resolution of the predicted location against our confidence about its accuracy. Our method can be used to estimate the error in document�s predicted location, enabling us to filter out poor quality predictions. We evaluate the proposed method exten- sively on user-generated content collected from two different social media sites, Flickr and Twitter. Our evaluation examines its perfor- mance on the geotagging task and with respect to different parame- ters. We achieve state-of-the-art results for all three tasks: location prediction, error estimation and result ranking and also provide a theoretical explanation of the effect of spatial resolution factor on geotagging accuracy. Our findings provide valuable insights into the design of geotagging systems and their quality control.

@inproceedings{Ghosh14kdd,
    abstract = {We study the interplay between a dynamic process and the structure of the
network on which it is defined. Specifically, we examine the impact of this
interaction on the quality-measure of network clusters and node centrality.
This enables us to effectively identify network communities and important nodes
participating in the dynamics. As the first step towards this objective, we
introduce an umbrella framework for defining and characterizing an ensemble of
dynamic processes on a network. This framework generalizes the traditional
Laplacian framework to continuous-time biased random walks and also allows us
to model some epidemic processes over a network. For each dynamic process in
our framework, we can define a function that measures the quality of every
subset of nodes as a potential cluster (or community) with respect to this
process on a given network. This subset-quality function generalizes the
traditional conductance measure for graph partitioning. We partially justify
our choice of the quality function by showing that the classic Cheeger's
inequality, which relates the conductance of the best cluster in a network with
a spectral quantity of its Laplacian matrix, can be extended from the
Laplacian-conductance setting to this more general setting.},
    author = {Ghosh, Rumi and Lerman, Kristina and Teng, Shang-Hua and Yan, Xiaoran},
    booktitle = {Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2014)},
    keywords = {social-networks},
    url = {http://arxiv.org/abs/1406.3387},
    title = {The Interplay Between Dynamics and Networks: Centrality, Communities, and Cheeger Inequality},
    year = {2014}
}

We study the interplay between a dynamic process and the structure of the network on which it is defined. Specifically, we examine the impact of this interaction on the quality-measure of network clusters and node centrality. This enables us to effectively identify network communities and important nodes participating in the dynamics. As the first step towards this objective, we introduce an umbrella framework for defining and characterizing an ensemble of dynamic processes on a network. This framework generalizes the traditional Laplacian framework to continuous-time biased random walks and also allows us to model some epidemic processes over a network. For each dynamic process in our framework, we can define a function that measures the quality of every subset of nodes as a potential cluster (or community) with respect to this process on a given network. This subset-quality function generalizes the traditional conductance measure for graph partitioning. We partially justify our choice of the quality function by showing that the classic Cheeger's inequality, which relates the conductance of the best cluster in a network with a spectral quantity of its Laplacian matrix, can be extended from the Laplacian-conductance setting to this more general setting.

@ARTICLE{Lerman14plosone,
  AUTHOR =       {Kristina Lerman and Tad Hogg},
  TITLE =        {Leveraging position bias to improve peer recommendation},
  JOURNAL =      {PLoS One},
  YEAR =         {2014},
  volume =       {9},
  number =       {6},
  pages =        {e98914},
  url = {http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0098914},
  urlBlog={http://crowdresearch.org/blog/?p=8881},
  abstract =     {With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases,  the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk.
We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.
},
  keywords =     {social-dynamics},
}

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.

@INPROCEEDINGS{Zhu14socialcom,
  AUTHOR =       {Linhong Zhu and Kristina Lerman},
  TITLE =        {A Visibility-based Model for Link Prediction in Social Media},
  BOOKTITLE =    {Proceedings of the ASE/IEEE Conference on Social Computing},
  YEAR =         {2014},
  abstract =     {A core task of social network analysis is to predict the formation of new social links. In the context of social media,
link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions
and the flow of information between users. Previous link prediction methods have generally represented the social
network as a graph and leveraged topological and semantic measures of similarity between two nodes to evaluate the
probability of link formation. In this work, we suggest another link creation mechanism for social media wherein a
user v creates a link to user u after seeing u�s name on his or her screen. In other words, visibility of a user (name) is
a necessary condition for new link formation. We propose a visibility-based model for link prediction, which estimates
the probability of a user views another user�s name, and use this model to predict new links. We further estimate
a set of parameters in the proposed visibility-based model by a Maximum-Likelihood approach with a MM algorithm.
Empirical study shows that the proposed model can more accurately predict both follow and co-mention links than alternative
state-of-the-art methods. Our work suggests that the effort required to discover a new social contact is negatively
correlated with link formation, and the easier it is to discover a user, the higher the likelihood a link to the user
will be created.},
   url = {http://www.isi.edu/integration/people/lerman/papers/SocialCom14.pdf},
  keywords =     {social-dynamics},
}

A core task of social network analysis is to predict the formation of new social links. In the context of social media, link prediction serves as the foundation for forecasting the evolution of the follower graph and predicting interactions and the flow of information between users. Previous link prediction methods have generally represented the social network as a graph and leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. In this work, we suggest another link creation mechanism for social media wherein a user v creates a link to user u after seeing u�s name on his or her screen. In other words, visibility of a user (name) is a necessary condition for new link formation. We propose a visibility-based model for link prediction, which estimates the probability of a user views another user�s name, and use this model to predict new links. We further estimate a set of parameters in the proposed visibility-based model by a Maximum-Likelihood approach with a MM algorithm. Empirical study shows that the proposed model can more accurately predict both follow and co-mention links than alternative state-of-the-art methods. Our work suggests that the effort required to discover a new social contact is negatively correlated with link formation, and the easier it is to discover a user, the higher the likelihood a link to the user will be created.

@ARTICLE{Ghosh14dcds,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis},
  JOURNAL =      {Discrete and Continuous Dynamical Systems Series B},
  YEAR =         {2014},
  volume =       {19},
  number =       {5},
  pages =        {1355 -- 1372},
  month =        {July},
  note =         {},
  abstract =     {Many popular measures used in social network analysis, including
centrality, are based on the random walk. The random walk is a model of a
stochastic process where a node interacts with one other node at a time. How-
ever, the random walk may not be appropriate for modeling social phenomena,
including epidemics and information diffusion, in which one node may interact
with many others at the same time, for example, by broadcasting the virus or
information to its neighbors. To produce meaningful results, social network
analysis algorithms have to take into account the nature of interactions be-
tween the nodes. In this paper we classify dynamical processes as conservative
and non-conservative and relate them to well-known measures of centrality
used in network analysis: PageRank and Alpha-Centrality. We demonstrate,
by ranking users in online social networks used for broadcasting information,
that non-conservative Alpha-Centrality generally leads to a better agreement
with an empirical ranking scheme than the conservative PageRank. http://arxiv.org/abs/1209.4616},
  keywords =     {social-networks},
    url = {http://aimsciences.org/journals/displayArticlesnew.jsp?paperID=9862},
}

Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. How- ever, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions be- tween the nodes. In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank. http://arxiv.org/abs/1209.4616

@inproceedings{Kooti14icwsm,
    abstract = {Social networks have many counter-intuitive properties, including the
"friendship paradox" that states, on average, your friends have more friends
than you do. Recently, a variety of other paradoxes were demonstrated in online
social networks. This paper explores the origins of these network paradoxes.
Specifically, we ask whether they arise from mathematical properties of the
networks or whether they have a behavioral origin. We show that sampling from
heavy-tailed distributions always gives rise to a paradox in the mean, but not
the median. We propose a strong form of network paradoxes, based on utilizing
the median, and validate it empirically using data from two online social
networks. Specifically, we show that for any user the majority of user's
friends and followers have more friends, followers, etc. than the user, and
that this cannot be explained by statistical properties of sampling. Next, we
explore the behavioral origins of the paradoxes by using the shuffle test to
remove correlations between node degrees and attributes. We find that paradoxes
for the mean persist in the shuffled network, but not for the median. We
demonstrate that strong paradoxes arise due to the assortativity of user
attributes, including degree, and correlation between degree and attribute.},
    author = {Kooti, Farshad and Hodas, Nathan O. and Lerman, Kristina},
    booktitle = {International Conference on Weblogs and Social Media (ICWSM)},
    keywords = {social-networks},
    month = mar,
    title = {Network Weirdness: Exploring the Origins of Network Paradoxes},
    url = {http://arxiv.org/abs/1403.7242},
    year = {2014},
    urlBlog={http://crowdresearch.org/blog/?p=8749}
}

Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user's friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.

@article{Hodas14srep,
    author = {Hodas, Nathan O. and Lerman, Kristina},
    doi = {10.1038/srep04343},
    journal = {Scientific Reports},
    keywords = {social-dynamics},
    title = {The Simple Rules of Social Contagion},
    urlPaper = {http://dx.doi.org/10.1038/srep04343},
    abstract={It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.},
    volume = {4},
    year = {2014}
}

It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.

@inproceedings{Zhu14sigmod,
    abstract = {The growing popularity of social media (e.g, Twitter) allows users to easily
share information with each other and influence others by expressing their own
sentiments on various subjects. In this work, we propose an unsupervised
\emph{tri-clustering} framework, which analyzes both user-level and tweet-level
sentiments through co-clustering of a tripartite graph. A compelling feature of
the proposed framework is that the quality of sentiment clustering of tweets,
users, and features can be mutually improved by joint clustering. We further
investigate the evolution of user-level sentiments and latent feature vectors
in an online framework and devise an efficient online algorithm to sequentially
update the clustering of tweets, users and features with newly arrived data.
The online framework not only provides better quality of both dynamic
user-level and tweet-level sentiment analysis, but also improves the
computational and storage efficiency. We verified the effectiveness and
efficiency of the proposed approaches on the November 2012 California ballot
Twitter data.},
    author = {Zhu, Linhong and Galstyan, Aram and Cheng, James and Lerman, Kristina},
    booktitle = {Proceedings of the ACM SIGMOD/PODS},
    keywords = {social-networks},
    title = {Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media},
    urlPaper = {http://arxiv.org/abs/1402.6010},
    year = {2014}
}

The growing popularity of social media (e.g, Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised \emphtri-clustering framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.

@INPROCEEDINGS{Kang13dubmod,
  AUTHOR =       {Jeon-hyung Kang and Kristina Lerman},
  TITLE =        {Scalable Mining of Social Data using Stochastic Gradient Fisher Scoring},
  BOOKTITLE =    {Proc. CIKM workshop on Data-driven User Behavioral Modelling and Mining from Social Media (DUBMOD)},
  YEAR =         {2013},
   urlPaper = {http://www.isi.edu/integration/people/lerman/papers/dubmod05-kang.pdf},
  keywords =     {social-annotation},
}

@inproceedings{Huang13icdm,
    author = {Yi-hung Huang and Chun-Nan Hsu and Kristina Lerman},
    booktitle = {Proc. of IEEE International Conference on Data Mining},
    keywords = {social-dynamics},
    title = {Identifying Transformative Scientific Research},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/ICDM13.pdf},
    year = {2013}
}

@article{Smith13spectral,
    abstract = {Spectral clustering is widely used to partition graphs into distinct modules
or communities. Existing methods for spectral clustering use the eigenvalues
and eigenvectors of the graph Laplacian, an operator that is closely associated
with random walks on graphs. We propose a new spectral partitioning method that
exploits the properties of epidemic diffusion. An epidemic is a dynamic process
that, unlike the random walk, simultaneously transitions to all the neighbors
of a given node. We show that the replicator, an operator describing epidemic
diffusion, is equivalent to the symmetric normalized Laplacian of a reweighted
graph with edges reweighted by the eigenvector centralities of their incident
nodes. Thus, more weight is given to edges connecting more central nodes. We
describe a method that partitions the nodes based on the componentwise ratio of
the replicator's second eigenvector to the first, and compare its performance
to traditional spectral clustering techniques on synthetic graphs with known
community structure. We demonstrate that the replicator gives preference to
dense, clique-like structures, enabling it to more effectively discover
communities that may be obscured by dense intercommunity linking.},
    journal = {Physical Review E},
  volume =       {88},
  number =       {4},
  pages =        {042813},
    author = {Smith, Laura M. and Lerman, Kristina and Garcia-Cardona, Cristina and Percus, Allon G. and Ghosh, Rumi},
    keywords = {social-networks},
    title = {Spectral Clustering with Epidemic Diffusion},
    UrlPaper = {http://arxiv.org/abs/1303.2663},
    year = {2013}
}

Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs. We propose a new spectral partitioning method that exploits the properties of epidemic diffusion. An epidemic is a dynamic process that, unlike the random walk, simultaneously transitions to all the neighbors of a given node. We show that the replicator, an operator describing epidemic diffusion, is equivalent to the symmetric normalized Laplacian of a reweighted graph with edges reweighted by the eigenvector centralities of their incident nodes. Thus, more weight is given to edges connecting more central nodes. We describe a method that partitions the nodes based on the componentwise ratio of the replicator's second eigenvector to the first, and compare its performance to traditional spectral clustering techniques on synthetic graphs with known community structure. We demonstrate that the replicator gives preference to dense, clique-like structures, enabling it to more effectively discover communities that may be obscured by dense intercommunity linking.

@inproceedings{Smith13socialcom,
    abstract = {In recent years, social media has revolutionized
how people communicate and share information. Twitter and
other blogging sites have seen an increase in political and
social activism. Previous studies on the behaviors of users in
politics have focused on electoral candidates and election results.
Our paper investigates the role of social media in discussing
and debating controversial topics. We apply sentiment analysis
techniques to classify the position (for, against, neutral) expressed
in a tweet about a controversial topic and use the results in
our study of user behavior. Our findings suggest that Twitter is
primarily used for spreading information to like-minded people
rather than debating issues. Users are quicker to rebroadcast
information than to address a communication by another user.
Individuals typically take a position on an issue prior to posting
about it and are not likely to change their tweeting opinion.},
    author = {Smith, Laura M. and Linhong Zhu and Kristina Lerman and Zornitsa Kozareva},
    booktitle = {ASE/IEEE International Conference on Social Computing},
    keywords = {social-dynamics},
    title = {The Role of Social Media in the Discussion of Controversial Topics},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/Smith13socialcom.pdf},
    year = {2013}
}

In recent years, social media has revolutionized how people communicate and share information. Twitter and other blogging sites have seen an increase in political and social activism. Previous studies on the behaviors of users in politics have focused on electoral candidates and election results. Our paper investigates the role of social media in discussing and debating controversial topics. We apply sentiment analysis techniques to classify the position (for, against, neutral) expressed in a tweet about a controversial topic and use the results in our study of user behavior. Our findings suggest that Twitter is primarily used for spreading information to like-minded people rather than debating issues. Users are quicker to rebroadcast information than to address a communication by another user. Individuals typically take a position on an issue prior to posting about it and are not likely to change their tweeting opinion.

@article{Hogg13socialcom,
    abstract = {User response to contributed content in online social media depends on many
factors. These include how the site lays out new content, how frequently the
user visits the site, how many friends the user follows, how active these
friends are, as well as how interesting or useful the content is to the user.
We present a stochastic modeling framework that relates a user's behavior to
details of the site's user interface and user activity and describe a procedure
for estimating model parameters from available data. We apply the model to
study discussions of controversial topics on Twitter, specifically, to predict
how followers of an advocate for a topic respond to the advocate's posts. We
show that a model of user behavior that explicitly accounts for a user
transitioning through a series of states before responding to an advocate's
post better predicts response than models that fail to take these states into
account. We demonstrate other benefits of stochastic models, such as their
ability to identify users who are highly interested in advocate's posts.},
    author = {Hogg, Tad and Lerman, Kristina and Smith, Laura M.},
    journal = {ASE HUMAN},
volume={2},
number={1},
    keywords = {social-dynamics},
    title = {Stochastic Models Predict User Behavior in Social Media},
    urlPaper = {http://arxiv.org/abs/1308.2705},
    year = {2013}
}

User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user transitioning through a series of states before responding to an advocate's post better predicts response than models that fail to take these states into account. We demonstrate other benefits of stochastic models, such as their ability to identify users who are highly interested in advocate's posts.

@inproceedings{Hodas13icme,
    abstract = {As the rate of content production grows, we must make a staggering number of
daily decisions about what information is worth acting on. For any flourishing
online social media system, users can barely keep up with the new content
shared by friends. How does the user-interface design help or hinder users'
ability to find interesting content? We analyze the choices people make about
which information to propagate on the social media sites Twitter and Digg. We
observe regularities in behavior which can be attributed directly to cognitive
limitations of humans, resulting from the different visibility policies of each
site. We quantify how people divide their limited attention among competing
sources of information, and we show how the user-interface design can mediate
information spread.},
    author = {Hodas, Nathan O. and Lerman, Kristina},
    keywords = {social-dynamics},
    title = {Attention and Visibility in an Information Rich World},
    booktitle = {Proceedings of the 2nd International ICME Workshop on Social Multimedia Research},
    urlPaper = {http://arxiv.org/abs/1307.4798},
    year = {2013}
}

As the rate of content production grows, we must make a staggering number of daily decisions about what information is worth acting on. For any flourishing online social media system, users can barely keep up with the new content shared by friends. How does the user-interface design help or hinder users' ability to find interesting content? We analyze the choices people make about which information to propagate on the social media sites Twitter and Digg. We observe regularities in behavior which can be attributed directly to cognitive limitations of humans, resulting from the different visibility policies of each site. We quantify how people divide their limited attention among competing sources of information, and we show how the user-interface design can mediate information spread.

@INCOLLECTION{Lerman13hcbook,
  AUTHOR =       {Kristina Lerman},
  editor =       {Pietro Michelucci},
  BOOKTITLE =        {Handbook of Social Computation},
  TITLE =      {Social Informatics: Using Big Data to Understand Social Behavior},
  pages =        {},
  PUBLISHER =    {Springer},
  YEAR =         {2013},
  address =      {},
  note =         {},
  keywords =     {social-dynamics},
}

@inproceedings{Narang13snakdd,
    author = {Kanika Narang and Kristina Lerman and Ponnurangam Kumaraguru},
    title = {Network Flows and the Link Prediction Problem},
    booktitle = {Proceedings of KDD workshop on Social Network Analysis (SNA-KDD)},
    keywords = {social-networks},
    year = {2013},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/Narang13snakdd.pdf},
}

@inproceedings{Kang13aaai,
    author = {Jeon-hyung Kang and Kristina Lerman},
    title = {LA-CTR: A Limited Attention Collaborative Topic Regression for Social Media},
    booktitle={Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI)},
    keywords = {social-annotation},
    year = {2013},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/AAAI2013.pdf},
    urlPresentation={https://www.dropbox.com/s/ayn36qipqkpl94u/AAAI13.ppt},
}

@inproceedings{Hodas13icwsm,
    abstract = {Feld's friendship paradox states that `your friends have more
friends than you, on average.' This paradox arises because extremely popular people,
despite being rare, are overrepresented when averaging over friends.
Using a sample of the Twitter firehose, we confirm that the friendship
paradox holds for >0.98 of Twitter users. Because of the directed nature of the
follower graph on Twitter, we are further able to confirm more detailed forms
of the friendship paradox: everyone you follow or who follows you has
more friends and followers than you. This is likely caused by a correlation
    we demonstrate between Twitter activity, number of friends, and number of
    followers. In addition, we discover two new paradoxes: the virality paradox
    that states `your friends receive more viral content than you, on average,'
    and the activity paradox, which states `your friends are more active than you, on average.'
    The latter paradox is important in regulating online communication. It may result in
    users having difficulty maintaining optimal incoming information rates,
    because following additional users causes the volume of incoming tweets to
    increase super-linearly. While users may compensate for increased
    information flow by increasing their own activity, users become information
    overloaded when they receive more information than they are able or
    willing to process. We compare the average size of cascades that are sent and
    received by overloaded and underloaded users. And we show that overloaded users
    post and receive larger cascades and they are poor detector of small cascades.},
    author = {Nathan O. Hodas and Farshad Kooti and Kristina Lerman},
    booktitle = {Proceedings of the 7Th International AAAI Conference On Weblogs And Social Media (ICWSM)},
    keywords = {social-networks},
    title = {Friendship Paradox Redux: Your Friends Are More Interesting Than You},
    year = {2013},
    url = {http://arxiv.org/abs/1304.3480},
    urlBlog={http://crowdresearch.org/blog/?p=7618},
    urlPresentation = {http://prezi.com/18rmya6ucitr/friendship-paradox-redux-icwsm-2013/},
    note={Honorable mention paper}
}

Feld's friendship paradox states that `your friends have more friends than you, on average.' This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends. Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >0.98 of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you. This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers. In addition, we discover two new paradoxes: the virality paradox that states `your friends receive more viral content than you, on average,' and the activity paradox, which states `your friends are more active than you, on average.' The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.

@inproceedings{Lerman13society,
    author = {Kristina Lerman and Prachi Jain and Rumi Ghosh and Jeon-Hyung Kang and Ponnurangam Kumaraguru},
    booktitle = {Proceedings of  International Conference on Social Intelligence and Technology (SOCIETY)},
    keywords = {social-networks},
    title = {Limited Attention and Centrality in Social Networks},
    year = {2013},
    urlPaper = {http://arxiv.org/abs/1303.4451},
}

@inproceedings{Kang13hypertext,
    author = {Jeon-hyung Kang and Kristina Lerman},
    booktitle = {Proceedings of  24th ACM Conference on Hypertext and Social Media (HyperText)},
    keywords = {social-networks},
    title = {Structural and Cognitive Bottlenecks to Information Access in Social Networks},
    urlPaper = {http://arxiv.org/abs/1303.0861},
    year = {2013},
}

@inproceedings{Kang13sbp,
    author = {Jeon-hyung Kang and Kristina Lerman and Lise Getoor},
    title = {LA-LDA: A Limited Attention Model for Social Recommendation},
    booktitle={International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP)},
    keywords = {social-annotation},
    year = {2013},
    note={(Terry Lyons Memorial Award for Best Student Paper)},
    urlPaper = {http://arxiv.org/abs/1301.6277},
    urlPresentation={https://www.dropbox.com/s/xafev7d92w7qxdl/SBP.ppt},
}

@inproceedings{Intagorn12cikm,
    author = {Suradej Intagorn and Kristina Lerman},
    title = {A Probabilistic Approach to Mining Geospatial Knowledge from Social Annotations},
    booktitle={Conference on Information and Knowledge Management (CIKM)},
    keywords = {social-annotation},
    year = {2012},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/sp199-intagorn.pdf}
}

@ARTICLE{Lerman12pre,
  AUTHOR =       {Kristina Lerman and Rumi Ghosh},
  TITLE =        {Network Structure, Topology and Dynamics in Generalized Models of Synchronization},
  JOURNAL =      {Physical Review E},
  YEAR =         {2012},
  volume =       {86},
  number =       {026108},
  pages =        {},
  month =        {},
    keywords = {social-networks},
  urlPaper = {http://arxiv.org/abs/1203.1338}
}

@inproceedings{Kang12homophily,
    author = {Jeon-hyung Kang  and Kristina Lerman},
    title = {Using Lists to Measure Homophily on Twitter},
    booktitle={AAAI workshop on  Intelligent Techniques for Web Personalization and Recommendation},
    keywords = {social-annotation},
    month = jul,
    year = {2012},
    urlPaper = {http://www.isi.edu/integration/people/lerman/papers/AAAI2012_homophily.pdf}
}

@inproceedings{Knoblock12,
    author = {Craig A. Knoblock and Pedro Szekely and Jose Luis Ambite and Aman Goel and Shubham Gupta and Kristina Lerman and Parag Mallick and Maria Muslea and
Mohsen Taheriyan},
    title = {Semi-Automatically Mapping Structured Sources into the Semantic Web},
    booktitle={Proceedings of Extended Semantic Web Conference},
    keywords = {semantic-modeling},
    year = {2012},
    urlPaper = {http://www.isi.edu/integration/papers/knoblock12-eswc.pdf},
}

@inproceedings{Garcia12,
    author = {Andr\'{e}s Garc\'{i}a-Silva and Jeon-Hyung Kang and Kristina Lerman and Oscar Corcho},
    title = {Characterising Emergent Semantics in Twitter Lists},
    booktitle={Proceedings of Extended Semantic Web Conference},
    keywords = {social-annotation},
    year = {2012}
}

@article{Lerman12empirical,
    author = {Kristina Lerman and Rumi Ghosh and Tawan Surachawala},
    journal={},
    keywords = {social-dynamics},
    title = {Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs},
    urlPaper = {http://arxiv.org/abs/1202.3162},
    year = {2012}
}

@ARTICLE{Hogg12epj,
  AUTHOR =       {Tad Hogg and Kristina Lerman},
  TITLE =        {Social Dynamics of Digg},
  JOURNAL =      {EPJ Data Science},
  YEAR =         {2012},
  volume =       {1},
  number =       {5},
  pages =        {},
  month =        jun,
  keywords =     {social-dynamics},
  urlPaper = {http://www.epjdatascience.com/content/1/1/5}
}

@inproceedings{Lerman12www,
    author = {Kristina Lerman and Suradej Intagorn and Jeon-hyung Kang and Rumi Ghosh},
    title = {Using Proximity to Predict Activity in Social Networks},
    booktitle={Proceedings of 21st International World Wide Web Conference (poster)},
    keywords = {social-networks},
    year = {2012},
    urlPaper = {http://arxiv.org/abs/1112.2755}
}

@inproceedings{Hodas12socialcom,
    author = {Nathan O. Hodas and Kristina Lerman},
    title = {How Limited Visibility and Divided Attention Constrain Social Contagion},
    booktitle={ASE/IEEE International Conference on Social Computing},
    keywords = {social-dynamics},
    urlPaper = {http://arxiv.org/abs/1205.2736},
    year = {2012}
}

@article{Ghosh12synchro,
    archivePrefix = {arXiv},
    author = {Rumi Ghosh and Kristina Lerman},
    keywords = {social-networks},
    month = jan,
    title = {The Role of Dynamic Interactions in Multi-scale Analysis of Network Structure},
    year = {2012},
    urlPaper = {http://arxiv.org/abs/1201.2383}
}

@ARTICLE{Lerman12tist,
  AUTHOR =       {Kristina Lerman and Tad Hogg},
  TITLE =        {Using Stochastic Models to Describe and Predict Social Dynamics of Web Users},
  JOURNAL =      {ACM Transactions on Intelligent Systems and Technology},
  YEAR =         {2012},
  volume =       {3},
  number =       {4},
  pages =        {},
  month =        {September},
  urlPaper={http://arxiv.org/abs/1010.0237},
  keywords =     {social-dynamics},
}

@inproceedings{Ghosh11commper,
author={Rumi Ghosh and Tsung-Ting Kuo and Chun-Nan Hsu and Shou-De Lin and Kristina Lerman},
booktitle={COMMPER 2011: Mining Communities and People Recommendations, Data Mining Workshops (ICDMW), 2010 IEEE International Conference on},
title={Time-aware Ranking in Dynamic Citation Networks},
year={2011},
month={December},
volume={},
number={},
pages={373 -380},
  keywords = {social-networks},
  urlPaper = {http://www.isi.edu/~lerman/papers/ecm_icdm.pdf}
}

@inproceedings{Knoblock11lisc,
  author = {Craig A. Knoblock, Pedro Szekely and Jose Luis Ambite and Shubham Gupta and Aman Goel and Maria Muslea and
  Kristina Lerman and Parag Mallick},
  title = {Interactively Mapping Data Sources into the Semantic Web},
  booktitle = {First International Workshop on Linked Science at the International Semantic Web Conference},
  year = {2011},
  keywords = {semantic-modeling},
  urlpaper = {}
}

@ARTICLE{Intagorn11ijiscram,
  AUTHOR =       {Suradej Intagorn and Kristina Lerman},
  TITLE =        {Mining Geospatial Knowledge on the Social Web},
  JOURNAL =      {International Journal of Information Systems for Crisis Response and Management},
  YEAR =         {2011},
  volume =       {3},
  number =       {2},
  pages =        {33--47},
  month =        {},
  keywords =     {social-annotation},
  urlpaper = {http://www.isi.edu/integration/people/lerman/papers/lerman2011ijiscram.pdf}
}

@ARTICLE{Ghosh11physrev,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {A Parameterized Centrality Metric for Network Analysis},
  JOURNAL =      {Physical Review},
  YEAR =         {2011},
  volume =       {E 83},
  number =       {6},
  pages =        {066118 },
  month =        {},
  keywords = {social-networks},
  urlPaper={http://arxiv.org/abs/1010.4247},
}

@inproceedings{Kang11socialcom,
  author = {Jeon-Hyung Kang and Kristina Lerman},
  title = {Leveraging User Diversity to Harvest Knowledge on the Social Web},
  booktitle = {Proceedings of the Third IEEE International Conference on Social Computing},
  year = {2011},
  keywords = {social-annotation},
  urlpaper = {http://www.isi.edu/integration/people/lerman/papers/kang11socialcom.pdf}
}

@inproceedings{Intagorn11acmgis,
  author = {Suradej Intagorn and Kristina Lerman},
  title = {Learning Boundaries of Vague Places from Noisy Annotations},
  booktitle = {Proceedings of 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems },
  year = {2011},
  keywords = {social-annotation},
  urlpaper = {http://www.isi.edu/integration/people/lerman/papers/intagorn2011acmgis.pdf}
}

@inproceedings{Ghosh11snakdd,
    author = {Rumi Ghosh and Surachawala, Tawan and Kristina Lerman},
    title = {Entropy-based Classification of Retweeting Activity on Twitter},
    booktitle = {Proceedings of KDD workshop on Social Network Analysis (SNA-KDD)},
    urlPaper = {http://arxiv.org/abs/1106.0346},
    urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/snakdd2011.pdf},
    keywords = {social-dynamics},
   month={August},
    year = {2011}
}

@inproceedings{Lerman11fosw,
    author = {Kristina Lerman and Aram Galstyan and Greg Ver Steeg and Tad Hogg },
    title = {Social Mechanics: An Empirically Grounded Science of Social Media},
    booktitle = {Proceedings for ICWSM workshop on the Future of Social Media (FOSW11)},
    keywords = {social-dynamics},
    urlPaper = {http://www.isi.edu/~lerman/papers/ICWSM11-FOSW.pdf},
    year = {2011}
}

@TECHREPORT{Ghosh11nonconservative,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman and Surachawala, Tawan and Voevodski, Konstantin and Teng, Shang-Hua},
  TITLE =        {{Non-Conservative} Diffusion and its Application to Social Network Analysis},
  INSTITUTION =  {University of Southern California},
    urlPaper = {http://arxiv.org/abs/1102.4639},
    year = {2011},
    keywords = {social-networks},
    archivePrefix = {arXiv},
    eprint = {1102.4639},
  month =        {Feb},
}

@inproceedings{Versteeg11icwsm,
    author = {{Greg {Ver Steeg}} and Rumi Ghosh and Kristina Lerman},
    title = {What stops social epidemics?},
    booktitle = {Proceedings of 5th International Conference on Weblogs and Social Media},
    keywords = {social-networks},
    note ={},
    urlPaper = {http://arxiv.org/abs/1102.1985},
    urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/icwsm2011web.pdf},
    year = {2011}
}

@INPROCEEDINGS{Helic11www,
  AUTHOR =       {Denis Helic and Markus Strohmaier and Christoph Trattner and Markus Muhr and Kristina Lerman},
  TITLE =        {Pragmatic Evaluation of Folksonomies},
  BOOKTITLE =    {Proceedings of 20th International World Wide Web Conference (WWW)},
  YEAR =         {2011},
  urlPaper={http://www.isi.edu/~lerman/papers/WWW2011_Folksonomies.pdf},
  keywords =     {social-annotation},
}

@inproceedings{Plangprasopchok11wsdm,
    author = {Anon Plangprasopchok and Kristina Lerman and Lise Getoor},
    title = {A Probabilistic Approach for Learning Folksonomies from Structured Data},
    booktitle = {Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)},
    keywords = {social-annotation},
    urlPaper = {http://arxiv.org/abs/1011.3557},
    month={February},
    year = {2011}
}

@inproceedings{Ghosh11wsdm,
    author = {Rumi Ghosh and Kristina Lerman},
    title = {A Framework for Quantitative Analysis of Cascades on Networks},
    booktitle = {Proceedings of Web Search and Data Mining Conference (WSDM)},
    keywords = {social-networks},
    urlPaper = {http://arxiv.org/abs/1011.3571},
   urlPresentation={},
   month={February},
    year = {2011}
}

@inproceedings{Kang10nips,
    author = {Kang, Jeon Hyung and Kristina Lerman},
    title = {Integrating Specialist and Folk Knowledge with Affinity Propagation},
    booktitle = {In Proceedings of NIPS workshop on Machine Learning for Social Computing},
    keywords = {social-annotation},
    urlPaper = {},
   urlPresentation={},
   month={December},
    year = {2010}
}

@inproceedings{Lerman10mlg,
    author = {Kristina Lerman and Rumi Ghosh and Kang, Jeon-hyung},
    title = {Centrality Metric for Dynamic Network Analysis},
    booktitle = {Proceedings of KDD workshop on Mining and Learning with Graphs (MLG)},
    keywords = {social-networks},
    urlPaper = {http://www.isi.edu/integration/papers/lerman10mlg.pdf},
    month={July},
    year = {2010}
}

@inproceedings{Ghosh10snakdd,
    author = {Rumi Ghosh and Kristina Lerman},
    title = {Predicting Influential Users in Online Social Networks},
    booktitle = {Proceedings of KDD workshop on Social Network Analysis (SNA-KDD)},
    keywords = {social-networks},
    urlPaper = {http://arxiv.org/abs/1005.4882},
   urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/SNAKDD10.pdf},
   month={July},
    year = {2010}
}

@INPROCEEDINGS{Plangprasopchok10kdd,
  AUTHOR =       {Anon Plangprasopchok and Kristina Lerman and Lise Getoor},
  TITLE =        {Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata},
  BOOKTITLE =    {Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD)},
  YEAR =         {2010},
  keywords =     {social-annotation},
    month={July},
 urlPaper={http://arxiv.org/abs/1005.5114},
  urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/sap-kdd10.pdf},
}

@INPROCEEDINGS{Plangprasopchok10starai,
  AUTHOR =       {Anon Plangprasopchok and Kristina Lerman and Lise Getoor},
  TITLE =        {From Saplings to A Tree: Integrating Structured Metadata via Relational Affinity Propagation},
  BOOKTITLE =    {Proceedings of the AAAI workshop on Statistical Relational AI},
  YEAR =         {2010},
  keywords =     {social-annotation},
    month={July},
 urlPaper={http://arxiv.org/abs/1005.4963},
}

@INPROCEEDINGS{Lerman10icwsm,
  AUTHOR =       {Kristina Lerman and Rumi Ghosh},
  TITLE =        {Information Contagion: an Empirical Study of Spread of News on Digg and Twitter Social Networks},
  BOOKTITLE =    {Proceedings of 4th International Conference on Weblogs and Social Media (ICWSM)},
  YEAR =         {2010},
  keywords =     {social-dynamics},
    month={May},
 urlPaper={http://arxiv.org/abs/1003.2664},
 urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/ICWSM.pdf},
}

@INPROCEEDINGS{Intagorn10iscram,
  AUTHOR =       {Suradej Intagorn and Anon Plangprasopchok and Kristina Lerman},
  TITLE =        {Harvesting Geospatial Knowledge from Social Metadata},
  BOOKTITLE =    {Proceedings of 7th International Conference on Information Systems for Crisis Response and Management (ISCRAM)},
  YEAR =         {2010},
  keywords =     {social-annotation},
    month={May},
  urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/ISCRAM.pdf},
  urlPaper={http://www.isi.edu/integration/papers/intagorn10-iscram.pdf},
}

@INPROCEEDINGS{Lerman10www,
  AUTHOR =       {Kristina Lerman and Tad Hogg},
  TITLE =        {Using a Model of Social Dynamics to Predict Popularity of News},
  BOOKTITLE =    {Proceedings of 19th International World Wide Web Conference (WWW)},
  YEAR =         {2010},
  urlPaper={http://www.isi.edu/~lerman/papers/wfp0788-lerman.pdf},
  urlPresentation={http://www.isi.edu/integration/people/lerman/presentations/WWW10.pdf},
  keywords =     {social-dynamics},
}

@ARTICLE{Plangprasopchok11tkdd,
  AUTHOR =       {Anon Plangprasopchok and Kristina Lerman},
  TITLE =        {Modeling Social Annotation: a Bayesian Approach},
  JOURNAL =      {ACM Transactions on Knowledge Discovery from Data},
	Volume = 5,
	Number = 1,
	pages =  4,
  YEAR =         {2010},
  urlPaper={http://arxiv.org/pdf/0811.1319},
  keywords =     {social-annotation},
}

@INPROCEEDINGS{Hogg09icwsm,
  AUTHOR =       {Tad Hogg and Kristina Lerman},
  TITLE =        {Stochastic Models of User-Contributory Web Sites},
  BOOKTITLE =    {Proceedings of 3rd International Conference on Weblogs and Social Media (ICWSM)},
  YEAR =         {2009},
  keywords =     {social-dynamics},
    month={May},
  urlPaper = {http://arxiv.org/pdf/0904.0016},
}

@INPROCEEDINGS{Ghosh09icwsm,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {Leaders and Negotiators: An Influence-based Metric for Rank},
  BOOKTITLE =    {Proceedings of 3rd International Conference on Weblogs and Social Media (Poster)},
  YEAR =         {2009},
  keywords =     {social-networks},
}

@INPROCEEDINGS{Ghosh09socialcom,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {Structure of Heterogeneous Networks},
  BOOKTITLE =    {Proceedings of 1st IEEE SIGCOM Social Computing Conference (SocialCom)},
  YEAR =         {2009},
  urlPaper={http://arxiv.org/pdf/0906.2212},
    month={August},
  keywords =     {social-networks},
}

@INPROCEEDINGS{Ambite09iswc,
  AUTHOR =       {Jose-Luis Ambite and Sirish Darbha and Aman Goel and Craig A. Knoblock and Kristina Lerman and Rahul Parundekar and Thomas A. Russ},
  TITLE =        {Automatically Constructing Semantic Web Services from Online Sources},
  BOOKTITLE =    {Proceedings of 8th International Semantic Web Conference},
  YEAR =         {2009},
    month={October},
  urlPaper={http://www.isi.edu/integration/papers/ambite09-iswc.pdf},
  keywords =     {semantic-modeling},
}

@InProceedings{ambite2009:ijcai,
  author = 	 {Jos\'{e} Luis Ambite and Bora Gazen and Craig A. Knoblock and Kristina Lerman and Thomas Russ},
  title = 	 {Discovering and Learning Semantic Models of Online Sources for Information Integration},
  booktitle = {Proceedings of the IJCAI Workshop on Information Integration on the Web},
  year = 	 {2009},
  address = 	 {Pasadena, CA},
  urlPaper={http://www.isi.edu/integration/papers/ambite09-iiweb.pdf},
  keywords =     {semantic-modeling},
    month={July},
}

@INPROCEEDINGS{Plangprasopchok09www,
  AUTHOR =       {Anon Plangprasopchok and Kristina Lerman},
  TITLE =        {Constructing Folksonomies from User-specified Relations on Flickr},
  BOOKTITLE =    {Proceedings of 18th International World Wide Web Conference (WWW)},
  YEAR =         {2009},
  keywords =     {social-annotation},
    month={May},
  urlPaper={http://www.isi.edu/~lerman/papers/fp500-plangprasopchok.pdf},
}

@INBOOK{Lerman09Handbook,
  AUTHOR =       {Kristina Lerman},
  editor =       {San Murugesan},
  TITLE =        {Handbook of Research on Web 2.0, 3.0, and X.0:
Technologies, Business, and Social Applications },
  CHAPTER =      {Leveraging User-specified Metadata to Personalize Image Search},
  pages =        {},
  PUBLISHER =    {IGI Global},
  YEAR =         {2009},
  keywords =     {social-annotation},
  urlPaper={http://www.isi.edu/integration/papers/personalization08.pdf},
}

@inproceedings{Lerman09aaaiss,
 author = {Kristina Lerman and T. Hogg},
 title = {Stochastic Models of Large-Scale Human Behavior on the Web},
 booktitle = {Proceedings of AAAI symposium on Modeling Human Behavior},
 year = {2009},
 location = {Stanford University},
 publisher = {AAAI},
 address = {Menlo Park, CA},
 keywords={social-dynamics},
 }

@InCollection{lerman09:eds,
  author = 	 "Kristina Lerman and Craig A. Knoblock",
  title = 	 "Wrapper Maintenance",
  publisher =	 "Springer",
  address =      "Leipzig, Germany",
  year =	 "2009",
  booktitle =    "Encyclopedia of Database Systems",
  editor =	 "Ling Liu and M. Tamer Ozsu",
  keywords =     {wrapper-maintenance},
  urlPaper={http://www.isi.edu/integration/papers/lerman09-eds.pdf},
}

@article{blythe08:jucs,
	Author = "Jim Blythe and Dipsy Kapoor and Craig A. Knoblock and Kristina Lerman and Steven Minton",
	Title = "Information Integration for the Masses",
	Journal =  "Journal of Universal Computer Science",
	Volume = 14,
	Number = 11,
	pages =      "1811--1837",
	Year = {2008},
	urlPaper={http://www.isi.edu/integration/papers/blythe08-jucs.pdf},
	keywords =     {semantic-modeling},
}

@INPROCEEDINGS{Ambite08sadm,
  AUTHOR =       {J.-L. Ambite and C. A. Knoblock and Kristina Lerman and Anon Plangprasopchok and T. Russ and
  C. Gazen and Steve Minton and M. J. Carman},
  TITLE =        {Exploiting Data Semantics to Discover, Extract, and Model Web Sources },
  BOOKTITLE =    {Proceedings of ICDM Workshop on Semantic Aspects of Data Mining},
  YEAR =         {2008},
  keywords =     {semantic-modeling},
}

@INPROCEEDINGS{Ghosh08,
  AUTHOR =       {Rumi Ghosh and Kristina Lerman},
  TITLE =        {Community Detection using a Measure of Global Influence},
  BOOKTITLE =    {Proceedings of the 2nd KDD Workshop on Social Network Analysis (SNAKDD'08)},
  YEAR =         {2008},
  urlPaper={http://arxiv.org/pdf/0805.4606},
  keywords =     {social-networks},
}

@INPROCEEDINGS{Lerman08wosn,
  AUTHOR =       {Kristina Lerman and Aram Galstyan },
  TITLE =        {Analysis of Social Voting Patterns on Digg},
  BOOKTITLE =    {Proceedings of the 1st ACM SIGCOMM Workshop on Online Social Networks},
  YEAR =         {2008},
  urlPaper={http://arxiv.org/pdf/0806.1918},
  keywords =     {social-dynamics},
}

@ARTICLE{Crespi08,
  author =       "V. Crespi and Aram Galstyan and Kristina Lerman",
  title =        "Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems",
  year =         "2008",
  JOURNAL =    "Autonomous Robots",
  volume =       {24},
  number =       {3},
  series =       {},
  pages =        {303--313},
  urlPaper =      {http://www.isi.edu/~lerman/papers/methodRevisedFinal.pdf},
  month =        {April},
  keywords =    {multi-agent-systems},
}

@INPROCEEDINGS{Shell07iros,
  AUTHOR =       {D. Shell and S. Viswanathan and  J. Huang and Rumi Ghosh and J. Huang and M. Matari{\'c}
  and Kristina Lerman and R. Sekuler},
  TITLE =        {Spatial Behavior of Individuals and Groups: Preliminary Findings from a Museum Scenario},
  BOOKTITLE =    {Proceedings of IROS workshop on Spatial Behavior Modeling},
  YEAR =         {2007},
  keywords =     {social-dynamics},
}

@INPROCEEDINGS{Lerman07sma,
  AUTHOR =       {Kristina Lerman},
  TITLE =        {User Participation in Social Media: Digg Study},
  booktitle =      {Proceedings of the WI/IAT workshop on Social Media Analysis },
  YEAR =         {2007},
  urlPaper={http://arxiv.org/abs/0708.2414},
  keywords =    {social-dynamics},
}

@INPROCEEDINGS{Blythe07iiweb,
  AUTHOR =       {Jim Blythe and Dipsy Kapoor and Craig A. Knoblock and Steve Minton and Kristina Lerman},
  TITLE =        {Information Intergation for the Masses},
  BOOKTITLE =    {Proceedings of the AAAI workshop on Information Integration on the Web},
  YEAR =         {2007},
  keywords =    {semantic-modeling},
}

@ARTICLE{Lerman07ic,
  AUTHOR =       {Kristina Lerman},
  TITLE =        {Social Information Processing in Social News Aggregation},
  JOURNAL =      {IEEE Internet Computing: special issue on Social Search},
  YEAR =         {2007},
  keywords =     {social-dynamics},
  urlPaper =       {http://arxiv.org/abs/cs.CY/0703087},
  volume =       {11},
  number =       {6},
  pages =        {16--28},

}

@article{Lerman07ijswis,
    author      = {Kristina Lerman and
               Anon Plangprasopchok and
               Craig A. Knoblock},
    journal     = {International Journal on Semantic Web and Information Systems, Special Issue on Ontology Matching},
    title       = {Semantic Labeling of Online Information Sources},
    volume = {3},
    number = {3},
    pages = {36--56},
    year        = {2007},
  keywords =    {semantic-modeling},
  urlPaper={http://www.isi.edu/integration/papers/lerman07-ijswis.pdf},
}

@INPROCEEDINGS{Lerman07snakdd,
  AUTHOR =       {Kristina Lerman},
  TITLE =        {Dynamics of Collaborative Document Rating Systems},
  BOOKTITLE =    {Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web Mining and Social Network Analysis},
  YEAR =         {2007},
  urlPaper = {http://www.isi.edu/~lerman/papers/rankdynamics3.pdf},
  keywords =    {social-dynamics},
}

@INPROCEEDINGS{Lerman07flickrsearch,
  AUTHOR =       {Kristina Lerman and Anon Plangprasopchok  and Michael C. Wong},
  TITLE =        {Personalizing Results of Image Search on Flickr},
  BOOKTITLE =    {Proceedings of AAAI workshop on Intelligent Techniques for Web Personlization},
  YEAR =         {2007},
  keywords =    {social-annotation},
  urlPaper =       {http://arxiv.org/abs/0704.1676},
}

@INPROCEEDINGS{Plangprasopchok07iiweb,
  AUTHOR =       {Anon Plangprasopchok and Kristina Lerman},
  TITLE =        {Exploiting Social Annotation for Resource Discovery},
  BOOKTITLE =    {Proceedings of AAAI workshop on Information Integration on the Web (IIWeb07)},
  YEAR =         {2007},
  keywords =    {social-annotation},
  urlPaper =    {http://arxiv.org/abs/0704.1675},
}

@INPROCEEDINGS{Lerman07digg,
  AUTHOR =       {Kristina Lerman},
  TITLE =        {Social Networks and Social Information Filtering on Digg},
  BOOKTITLE =    {Proceedings of 1st International Conference on Weblogs and Social Media (ICWSM-07)},
  YEAR =         {2007},
  keywords =    {social-dynamics},
  urlPaper= {http://arxiv.org/abs/cs.HC/0612046}
}

@INPROCEEDINGS{Lerman07flickr,
  AUTHOR =       {Kristina Lerman and Laurie Jones},
  TITLE =        {Social Browsing on Flickr},
  BOOKTITLE =    {Proceedings of 1st International Conference on Weblogs and Social Media (ICWSM-07)},
  YEAR =         {2007},
  keywords =    {social-dynamics},
  urlPaper= {http://arxiv.org/abs/cs.HC/0612047}
}

@INPROCEEDINGS{Lerman06aaai,
  AUTHOR =       {Kristina Lerman and Anon Plangprasopchok and Craig A. Knoblock},
  TITLE =        {Automatically Labeling the Inputs and Outputs of Web Services},
  BOOKTITLE =    {Proceedings of National Conference on Artificial Intelligence (AAAI-06)},
  YEAR =         {2006},
  urlPaper = {http://www.isi.edu/integration/papers/lerman06-aaai.pdf},
  keywords =    {semantic-modeling},
}

@ARTICLE{Lerman06ijrr,
  author =       {Kristina Lerman and Chris V. Jones and Aram Galstyan and Maja J. Matari{\'c} },
  title =        {Analysis of Dynamic Task Allocation in Multi-Robot Systems},
  journal =      {International Journal of Robotics Research},
  year =         {2006},
  volume =       {25},
  number =       {3},
  pages =        {225--242},
  urlPaper={http://www.isi.edu/~lerman/papers/TaskAllocationPaper.pdf},
  keywords =    {multi-agent-systems},
}

@INPROCEEDINGS{Hall05,
  AUTHOR =       {Ewa Deelman and Aram Galstyan and Yolanda Gil and M. Hall and Kristina Lerman and Aichiro Nakano and Priya Vashista and Joel Saltz},
  TITLE =        {A Systematic Approach to Composing and Optimizing Application Workflows},
  BOOKTITLE =    {Proceedings of  Workshop on Patterns in High Performance Computing},
  YEAR =         {2005},
  keywords =     {systems},
}

@InProceedings{Galstyan05sis,
  author =       "Aram Galstyan and T. Hogg and Kristina Lerman",
  title =        "Modeling and mathematical analysis of swarms of microscopic robots",
  year =         "2005",
  month="jun",
  booktitle =    "Proceedings of IEEE Swarm Intelligence Symposium (SIS-2005), Pasadena, CA",
  urlPaper =        {http://arxiv.org/abs/cs/0604110},
  keywords =    {multi-agent-systems},
}

@InProceedings{Crespi05aamas,
  author =       "Valentino Crespi and Aram Galstyan and Kristina Lerman",
  title =        "Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems",
  year =         "2005",
  month="jun",
  booktitle =    "Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems ({AAMAS}-2005) (poster), Utrecht, Netherlands",
  pages =        "",
  keywords =    {multi-agent-systems},
}

@incollection{Lerman05sab,
        author = {Kristina Lerman and A. Martinoli and Aram Galstyan},
        title = {A Review of Probabilistic Macroscopic Models for Swarm
Robotic Systems},
        booktitle = {Swarm Robotics Workshop: State-of-the-art Survey},
        year = {2005},
        editor = {Sahin E. and Spears W.},
        series = "LNCS",
        number = {3342},
        pages = {143--152},
        address = {Berlin Heidelberg},
        publisher = {Springer-Verlag},
        urlPaper={http://www.isi.edu/~lerman/papers/lerman-isab04.pdf},
    annote = {SAB workshop on Swarm Robotics, Santa Monica, CA, July 2004},
  keywords =    {multi-agent-systems},
}

@incollection{Galstyan04esos,
        author = {Aram Galstyan and Kristina Lerman},
        title = {Analysis of a Stochastic Model of Adaptive Task Allocation in Robots},
        booktitle = {Engineering Self Organizing Systems: Methodology and Applicationsy},
        year = {2005},
        editor = {S. Breuckner and G. Di Marzo Serugendo and A. Karageorgos and R. Nagpal},
        series = "LNAI",
        number = {3464},
        pages = {167},
        address = {Berlin Heidelberg},
        publisher = {Springer-Verlag},
    annote = {AAMAS-2004 Workshop, NYC, July 2004},
  keywords =    {multi-agent-systems},
}

@ARTICLE{Lerman04adaptive,
  author =       {Kristina Lerman},
  title =        {A Model of Adaptation in Collaborative Multi-Agent Systems},
  journal =      {Adaptive Behavior},
  year =         {2004},
  volume =       {12},
  number =       {3--4},
  pages =        {187--198},
  urlPaper={http://www.isi.edu/~lerman/papers/lerman05adaptive.pdf},
  annote =       {Same as Lerman03insects, MASI-03 workshop, Atlanta, GA, December 2003.},
  keywords =    {multi-agent-systems},
}

@InProceedings{Lerman04atem,
  author =       "Kristina Lerman and C. Gazen  and Steve Minton and Craig A. Knoblock",
  title =        "Populating the Semantic Web",
  pages =        "",
  ISBN =         "",
  booktitle =    "Proceedings of the Workshop on Advances in Text Extraction and Mining ({AAAI}-2004)",
  month =        "",
  publisher =    "",
  address=       "",
  year =         "2004",
  keywords =    {semantic-modeling},
}

@INBOOK{Lerman04ve,
  author  =      {Kristina Lerman and Steve Minton and Craig A. Knoblock},
  title =        {Virtual Enterprise Integration: Technological and Organizational Perspective},
  chapter =      {Machine Learning Techniques for Web Wrapper Maintenance, Goran D. Putnik and
Maria Manuela Cunha (Eds)},
  pages =        {},
  publisher =    {Idea Group, Hershey, PA},
  year =         {2004},
  keywords =    {wrapper-maintenance},
}

@InProceedings{Galstyan04aamas,
  author =       "Aram Galstyan and Karl Czajkowski and Kristina Lerman",
  title =        "{Resource Allocation in the Grid Using Reinforcement Learning}",
  year =         "2004",
  month="Jul",
  booktitle =    "Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems ({AAMAS}-2004) (poster), New York, NY",
  pages =        "1314-1315",
  keywords =    {multi-agent-systems},
}

@InProceedings{Galstyan04ipsn,
  author =       "Aram Galstyan and Bhaskar Krishnamachari and Sundeep Pattem and Kristina Lerman",
  title =        "{Distributed Online Localization in Sensor Networks Using a Moving Target}",
  month={apr},
  year =         "2004",
  booktitle =    "Information Processing in Sensor Networks (IPSN-2004), Berkeley, CA",
  pages =        "",
  keywords =    {multi-agent-systems},
}

@InProceedings{Lerman04sigmod,
  author =       "Kristina Lerman and L. Getoor and Steve Minton and Craig A. Knoblock",
  title =        "{Using the Structure of Web Sites for Automatic Segmentation of Tables}",
  month={jun},
  year =         "2004",
  booktitle =    "In \emph{Proceedings of ACM SIG on Management of Data (SIGMOD-2004)}",
  pages =        "",
  urlPaper={http://www.isi.edu/~lerman/papers/lermanSIGMOD04.pdf},
  keywords =    {wrapper-maintenance},
}

@incollection{Lerman02nasa,
  author =       "Kristina Lerman and Aram Galstyan",
  editor =       "Kagan Tumer and David Wolpert",
  title =        "Collectives and Design of Complex Systems",
  chapter =      "Two Paradigms for the Design of Artificial Collectives",
  pages =        "231--256",
  publisher =    "Springer Verlag",
  year =         "2004",
  volume =       "",
  number =       "",
  series =       "",
  address =      "New York",
  annote = "NASA-Ames August 2002 workshop",
  keywords =    {multi-agent-systems},
}

@InProceedings{Lerman03iros,
  author =       "Kristina Lerman and Aram Galstyan",
  title =        "{Macroscopic Analysis of Adaptive Task Allocation in Robots}",
  month={oct},
  year =         "2003",
  booktitle =    "Proceedings of the IEEE International Conference on Intelligent Robots and Systems ({IROS}-2003), Las Vegas, NV",
  pages =        "1951--1956",
  keywords =    {multi-agent-systems},
}

@InProceedings{Bugacov+al03,
  author =       "Alejandro Bugacov and Aram Galstyan  and Kristina Lerman",
  title =        "{Threshold Behavior in Boolean Network Model for SAT}",
  year =         "2003",
  month= "jun",
  booktitle =    "Proceedings of the International Conference on Artificial Intelligence ({IC-AI}-2003), Las Vegas, NV",
  pages =        "87--92",
  urlPaper={http://www.isi.edu/~lerman/papers/satbn.pdf},
  keywords =    {multi-agent-systems},
}

@InProceedings{Lerman03aamas,
  author =       "Kristina Lerman and Aram Galstyan",
  title =        "{Agent Memory and Adaptation in Multi-Agent Systems}",
  year =         "2003",
  month="Jul",
  booktitle =    "Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems ({AAMAS}-2003), Melbourne, Australia",
  pages =        "797--803",
  urlPaper={http://www.isi.edu/~lerman/papers/lerman-aamas03.pdf},
  keywords =    {multi-agent-systems},
}

@InProceedings{Galstyan03aamas,
  author =       "Aram Galstyan  and  S. Kolar and Kristina Lerman",
  title =        "{Resource Allocation Games with Changing Resource Capacities}",
  year =         "2003",
  booktitle =    "Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems ({AAMAS}-2003), Melbourne, Australia",
  pages =       "145--152",
  keywords =    {multi-agent-systems},
}

@ARTICLE{LermanJAMAS,
  author =       {Kristina Lerman and Aram Galstyan and T. Hogg},
  title =        {A Methodology for Mathematical Analysis of Multi-Agent Systems},
  journal =      {unpublished},
  year =         {2003},
  keywords =    {multi-agent-systems},
    urlPaper={http://arxiv.org/abs/cs/0404002},
}

@ARTICLE{Lerman03jair,
  author =       {Kristina Lerman and Steven Minton and Craig Knoblock},
  title =        {Wrapper Maintenance: A Machine Learning Approach},
  journal =      {Journal of Artificial Intelligence Research},
  year =         {2003},
  volume =       {18},
  number =       {},
  pages =        {149--181},
  urlPaper={http://www.isi.edu/integration/papers/lerman03-jair.pdf},
  keywords =    {wrapper-maintenance},
}

@ARTICLE{Galstyan02,
  author =       {Aram Galstyan and Kristina Lerman},
  title =        {Adaptive Boolean Networks and Minority Games with Time-Dependent Cutoffs},
  journal =      {Physical Review},
  year =         {2002},
  volume =       {E66},
  number =       {},
  pages =        {015103},
  month =        {},
  urlPaper={http://www.isi.edu/~lerman/papers/mg.pdf},
  keywords =    {multi-agent-systems},
}

@ARTICLE{Lerman02,
  author =       {Kristina Lerman and Aram Galstyan},
  title =        {Mathematical Model of Foraging in a Group of Robots: Effect of Interference},
  journal =      {Autonomous Robots},
  year =         {2002},
  volume =       {13},
  number =       {2},
  pages =        {127--141},
  month =        {},
  urlPaper={http://www.isi.edu/~lerman/papers/foraging.pdf},
  keywords =    {multi-agent-systems},
}

@Article{ElvesAIM,
  author =       "H. Chalupsky and Y. Gil and Craig A. Knoblock and Kristina Lerman and J. Oh and D.V. Pynadath and T.A. Russ and M. Tambe",
  title =        "Electric Elves: Applying Agent Technology to Support Human Organizations",
  pages =        "11--24",
  journal =      "AI Magazine",
  year =         "2002",
  volume =       {23},
  number =       {2},
  annote =    {also in Proceedings of the Thirteenth Annual Conference on Innovative Applications of
                    Artificial Intelligence ({IAAI}-2001), Seattle, WA},
  keywords =    {semantic-modeling},
  }

@ARTICLE{Lerman01,
  author =       {Kristina Lerman and Aram Galstyan and A. Martinoli and A. Ijspeert},
  title =        {A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems},
  journal =      {Artificial Life Journal},
  year =         {2001},
  volume =       {7},
  number =       {4},
  pages =        {375--393},
  urlPaper={http://www.isi.edu/~lerman/papers/lerman-alife.pdf},
  keywords =    {multi-agent-systems},
}

@TECHREPORT{LermanTR01,
  author =       {Kristina Lerman and Aram Galstyan },
  title =        {A General Methodology for Mathematical Analysis of Multi-Agent Systems},
  institution =  {University of California, Information Sciences Institute},
  year =         {2001},
  number =       {ISI-TR-529},
  address =      {},
  month =        {},
  urlPaper =         {http://www.isi.edu/isi-technical-reports.html},
  keywords =    {multi-agent-systems},
}

@InProceedings{Lerman-ATEM01,
  author =       "Kristina Lerman and Craig A. Knoblock and Steve Minton",
  title =        "Automatic Data Extraction from Lists and Tables in Web Sources",
  pages =        "",
  ISBN =         "",
  booktitle =    "Proceedings of the workshop on Advances in Text Extraction and Mining ({IJCAI}-2001)",
  month =        "",
  publisher =    "AAAI Press",
  address=       "Menlo Park",
  year =         "2001",
  urlPaper={http://www.isi.edu/~lerman/papers/lerman-atem2001.pdf},
  keywords =    {semantic-modeling},
}

@Article{KLMM2001,
  author =       "Craig A. Knoblock and Kristina Lerman and Steve Minton and I. Muslea",
  title =        "Accurately and reliably extracting data from the web: A machine learning approach",
  journal =      "IEEE Data Engineering Bulletin",
  year =         "2001",
  volume =       {23},
  number =       {4},
  pages =        {33--41},
  keywords =    {wrapper-maintenance},
}

@InProceedings{KLerman00,
  author =       "Kristina Lerman and Steven Minton",
  title =        "Learning the Common Structure of Data",
  pages =        "",
  ISBN =         "",
  booktitle =    "Proceedings of the 15th National Conference on
                 Artificial Intelligence ({AAAI}-2000)",
  month =        Jul # "~26--30",
  publisher =    "AAAI Press",
  address =      "Menlo Park",
  year =         "2000",
  urlPaper={http://www.isi.edu/~lerman/papers/KLerman00.pdf},
  keywords =    {semantic-modeling},
}

@InProceedings{Lerman00,
  author =       "Kristina Lerman and Onn Shehory",
  title =        "{Coalition Formation for Large-Scale Electronic Markets}",
  year =         "2000",
  booktitle =          "Proceedings of the International Conference on Multi-Agent Systems ({ICMAS}'2000), Boston, MA, 2000.",
  pages =       "167--174",
  urlPaper={http://www.isi.edu/~lerman/papers/LerShe99.pdf},
  keywords =    {multi-agent-systems},
}