Artificial Intelligence

“Predicting human behaviors in techno-social systems: fighting abuse and illicit activities”

When:
Friday, April 03, 2015, 11:00am - 12:00pm PDTiCal
Where:
11th Floor Conf Rm (#1135)
This event is open to the public.
Type:
AI Seminar
Speaker:
Emilio Ferrara (Indiana University)
Description:

Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=0de28f610b2344099f975...

Abstract:

The increasing availability of data across different socio-technical systems, such as online social networks, social media, and mobile phone networks, presents novel challenges and intriguing research opportunities. As more online services permeate through our everyday life and as data from various domains are connected and integrated with each other, the boundary between the real and the online worlds becomes blurry. Such data convey both online and offline activities of people, as well as multiple time scales and resolutions.

In this talk, I'll discuss my research efforts aimed at characterizing and predicting human behaviors and activities in techno-social worlds: starting by discussing network structure and information spreading on large social networks, I'll move toward characterizing entire online conversations, such as those around big real-world events, to capture the dynamics driving the emergence of collective attention and trending topics. I'll describe a machine learning framework leveraging these insights to detect promoted campaigns that mimic grassroots conversation. Aiming at learning the signature of abuse at the level of the single individuals, I'll illustrate the challenges posed by characterizing human activity as opposed to that of synthetic entities (social bots) that attempt emulate us, to persuade, smear, tamper or deceive. I'll draw a parallel with detecting illicit activities in the real world leveraging the traces left by criminals' interactions via mobile phones.

I'll conclude envisioning the design of computational systems that will help us making effective, timely decisions (informed by social data), and create actionable policies to contribute create a better future society.

Bio:

Emilio Ferrara is research assistant professor at the School of Informatics and Computing of Indiana University, where he teaches I400/590 Mining the Social Web, and research scientist at the IU Network Science Institute. He holds a PhD in Mathematics & Computer Science with honors [University of Messina (IT), program ranked 2nd in Italy, top100 worldwide]. During his PhD years he was a visiting scholar at the Vienna University of Technology and at the Royal Holloway University of London. He was a postdoctoral fellow of the Center for Complex Networks and Systems Research at Indiana University, working with Alessandro Flammini and Fil Menczer for 2.5 years. He lead the DARPA/SMISC project on campaigns and social bots detection, and the DARPA Social Bot Detection Challenge for the IU team.

Emilio’s research interests lie at the intersection between Network Science, Data Science, Machine Learning, and Computational Social Science. His work explores Social Networks and Social Media Analysis, Criminal Networks, and Knowledge Engineering. His research appears on top journals like Communications of the ACM and Physical Review Letters, and on several ACM and IEEE Transactions Journals and Conference Proceedings.

He is Lead Guest Editor of the EPJ Data Science thematic series on Collective Behaviors and Networks, and serves in the Program Committees of several prestigious conferences like WWW, ICWSM, and SocInfo. Emilio is co-chair of various workshops recurring at ECCS, WWW, SocInfo, and WebScience; he was also the local & sponsor chair of ACM Web Science 2014 and publicity co-chair of SocInfo 2014.

His work has been featured on tech and business magazines like MIT Technology Review, TIME, New Scientist, Fast Company, Engadget, Wired, and Mashable, and on the popular press including on the Guardian, the Washington Post, the Seattle Times, the Atlantic, and BBC! 

Emilio is a top 0.5% Kaggle competitor and enjoys participating to various data science competitions.

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