University of Southern California

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Kristina Lerman
  Science of Innovation  
  Social and economic progress is driven by scientific discoveries, and technological and legal inno- vations. However, as new knowledge accumulates at an accelerating pace through the publication of new scientific papers, patent applications and legal opinions, it becomes ever more difficult to identify knowledge that is critical for innovation. In order to create tools to speed up innovation, we first need to understand how people find and evaluate knowledge. The goal of this project is to learn how scholars and innovators discover and evaluate knowledge through an empirical analysis of citation networks in three domains - physics papers, patents and federal court opinions.  
  The central tenet of this project is that knowledge discovery and valuation is a human activity, and as such, it is subject to cognitive heuristics, or mental shortcuts, that help people make quick decisions about what to pay attention to. Behavioral data for studying the impact of cognitive heuristics decisions is available in the form of navigation traces of information networks, including citation networks. These networks capture the decisions that scholars and innovators made about what relevant documents to reference in their own work. By conducting comparative empirical analysis of citations made by physics papers, patents, and federal court decisions, this project will identify the strategies people use to decide what information to attend to, especially under conditions of information overload, and study the interplay between these strategies, the quality of information, and the decisions of others. By comparing citations to human navigation of other information networks, such as Wikipedia, USC researchers are helping uncover the common factors affecting knowledge discovery in information networks. The new understanding of the role of cognitive heuristics in citation will inform the design of next generation knowledge discovery tools that will help people to more optimally leverage citations to improve the efficiency and robustness of discovery and innovation.  

  Wu, H. and Lerman, K. Deep Context: A Neural Language Model for Large-scale Networked Documents. In IJCAI, 2017.
  Lamprecht, D.; Lerman, K.; Helic, D.; and Strohmaier, M. How the structure of Wikipedia articles influences user navigation. New Review of Hypermedia and Multimedia, 1--22. May 2016.
  Geigl, F.; Lerman, K.; Walk, S.; Strohmaier, M.; and Helic, D. Assessing the Navigational Effects of Click Biases and Link Insertion on the Web. In Hypertext Conference, 2016.
  This research was generously supported by the National Science Foundation under Grant No. BCS-1360058.