Talk Title: Fighting Poverty with Data?
Abstract: In wealthy nations, novel sources of “big data” from the internet and social media are creating new opportunities for commercial profit, enabling new approaches to social science research, and inspiring new perspectives on public policy. In developing economies, however, fewer sources of robust data exist, and it remains unclear if and how the world’s poor will benefit from the “data revolution.” In this talk, I will discuss a series of studies that combine insights from machine learning with traditional methods in empirical economics to provide a new perspective on economic growth and development. The talk will focus on recent results from Afghanistan, Ghana, and Rwanda, which show how terabyte-scale data from satellites and mobile phone networks can be combined with field-based experiments and surveys to model poverty and vulnerability. In resource-constrained environments where censuses and household surveys are rare, this creates options for gathering localized and timely information at a fraction of the cost of traditional methods.
Bio: Joshua Blumenstock is an Assistant Professor at the UC Berkeley School of Information. His research develops theory and methods for the analysis of large-scale behavioral data, with a focus on how such data can be used to better understand poverty and economic development. Recent projects combine field experiments with big spatiotemporal network data to model decision-making in poor and conflict-affected regions of the world. Prior to joining Berkeley, Joshua was on the faculty at the University of Washington, where he founded and co-directed the Data Science and Analytics Lab. He has a Ph.D. in Information Science and a M.A. in Economics from UC Berkeley, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University. He is a recipient of the Intel Faculty Early Career Honor, a Gates Millenium Grand Challenge award, a Google Faculty Research Award, and a former fellow of the Thomas J. Watson Foundation and the Harvard Institutes of Medicine.
KAIST and Facebook
Talk Title: How fast can we detect rumors?
Abstract: Social platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand rumor propagation processes. In detecting rumors in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early rumor detection. I will also highlight the latest rumor detection studies with deep learning techniques.
Bio: Meeyoung Cha is an associate professor at Graduate School of Culture Technology in KAIST and currently a Visiting Professor at Facebook. Her research interests are in the analysis of large-scale online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist.
Talk Title: The Reasonable Effectiveness of Roles in Complex Networks
Abstract: Given a network, how can we automatically discover roles (or functions) of nodes? Roles compactly represent structural behaviors of nodes and generalize across various networks. Examples of roles include “clique-members,” “periphery-nodes,” “bridges,” etc. Are there good features that we can extract for nodes that indicate role-membership? How are roles diffevent from communities and from equivalences (from sociology)? What are the applications in which these discovered roles can be effectively used? In this talk, we address these questions, provide unsupervised and supervised algorithms for role discovery, and discuss why roles are so effective in many applications from transfer learning to re-identification to anomaly detection to mining time-evolving networks and multi-relational graphs.
Bio: Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.
Project Leader / Research Associate Professor
USC Information Sciences Institute
Talk Title: Cognitive Heuristics and the Limits of Crowd Wisdom
Abstract: The many decisions people make about what information to attend to affect emerging trends, the diffusion of information in social media, and performance of crowdsourcing applications. Due to constraints of available time and cognitive resources, the ease of discovery strongly affects how people allocate their attention. Through empirical analysis and online experiments, we identify some of the cognitive heuristics that influence individual decisions to allocate attention to online content and quantify their impact on individual and collective performance. Specifically, we show that the position of information in the user interface determines whether it will be seen, while explicit social signals about its popularity increase the likelihood of response. Moreover, these heuristics become even more important in explaining and predicting behavior as cognitive load increases. These findings suggest that cognitive heuristics and information overload bias collective outcomes and undermine the “wisdom of crowds” effect.
Bio: Kristina Lerman is a Project Leader at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. Trained as a physicist, she now applies network- and machine learning-based methods to problems in social computing and social media analysis.
Talk Title: Social Research in the Age of Big Data
Abstract: The digital age has transformed how researchers are able to study social behavior. These new opportunities mean that the future of social research will involve blending together insights from two communities: social scientists and data scientists. In this talk, I’ll begin by describing what I think each community has to contribute and what each community has to learn. Then, I’ll focus on this social science/data science hybrid in one particular domain where I see a lot of opportunities: survey research. The talk will conclude with some predictions about the future of social research. This talk is based on my forthcoming book Bit by Bit: Social Research in the Digital Age, http://www.bitbybitbook.com.
Bio: Matthew Salganik is Professor of Sociology at Princeton University, and he is affiliated with several of Princeton’s interdisciplinary research centers: the Office for Population Research, the Center for Information Technology Policy, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. During the 2015-16 academic year, he will be Visiting Professor at Cornell Tech. Salganik’s research has been published in journals such as Science, PNAS, Sociological Methodology, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times, Wall Street Journal, Economist, and New Yorker. Salganik’s research is funded by the National Science Foundation, National Institutes of Health, Joint United Nations Program for HIV/AIDS (UNAIDS), Facebook, and Google.
Microsoft Research & University of Massachusetts Amherst
Bio:Hanna Wallach is a senior researcher at Microsoft Research New York City and an adjunct associate professor in the College of Computer Science at the University of Massachusetts Amherst. She is also a member of UMass’s Computational Social Science Institute. She has a BA in computer science from the University of Cambridge, an MS in cognitive science from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge. Her research is in the interdisciplinary field of computational social science. She develops machine learning methods for uncovering new insights about the ways in which people interact. She collaborates with political scientists, sociologists, and journalists to learn how organizations work in practice by analyzing publicly available data, such as public record email networks, document dumps, press releases, meeting transcripts, and news articles.