Wednesdays@NICO | 12:00-1:00 PM, February 10, 2016 | Chambers Hall, Lower Level

Predicting Human Behavior in Techno-Social Systems: Fighting Abuse and Illicit Activities

Emilio Ferrara, Computer Scientist, Information Sciences Institute, University of Southern California

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 behavior and activities in techno-social worlds: starting by discussing network structure and information spreading on large online 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
Dr. Emilio Ferrara is a Computer Scientist at the USC's Information Sciences Institute. Ferrara's research interests include designing machine-learning systems to model and predict individual behavior in techno-social systems, characterize information diffusion and information campaigns, and predict crime and abuse in such environments. He has held research positions in institutions in Italy, Austria, and UK (2009-2012). Before joining USC in 2015, he was a Research Assistant Professor at the School of Informatics and Computing of Indiana University (2012-2015). Ferrara earned a Ph.D. in Mathematics and Computer Science from University of Messina (Italy), and has published over 60 articles on machine learning, network science, and social media, appeared in top venues including PNAS, Communications of the ACM, Physical Review Letters, and several ACM and IEEE transactions and top conferences (WWW, CSCW, etc.). His research on social network abuse and crime prediction has been featured on the major news outlets (TIME, BBC, The New York Times, etc.) and tech magazines (MIT Technology Review, Vice, Mashable, New Scientist, etc). His research has been supported by DARPA, ONR, and IARPA. Ferrara is Guest Editor of two special issues on network science and computational social sciences, published respectively on EPJ Data Science and Future Internet. He's member of the PC for conferences including ACM WWW, ICWSM, and SocInfo. Ferrara is co-chair of workshops recurring at ECCS, WWW, SocInfo, and WebScience; he was Local & Sponsor Chair of ACM Web Science 2014 and Publicity co-chair of SocInfo 2014. In 2015, Ferrara was named IBM Watson Analytics VIP Influential in Big Data.

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