Computational Social Science Institute

University of Massachusetts Amherst

Lecture series generously sponsored by Yahoo!

Videos of some CSSI seminars are available  here

Spring 2014

What Can Twitter Tell Us About the "Real World"?
Monday, February 24, 2014 • 12:00PM-2:00PM • Lunch provided
Computer Science Bulding, Room 150/151

Abstract:   Due to Twitter's global popularity and the relative ease with which large amounts of tweets can be collected and analyzed, more and more researchers turn to Twitter as a data source for studies in Computational Social Science. But at the same time it is obvious that Twitter users are not representative of the overall population. So the question arises what Twitter can really tell us about the "Real World" beyond teens' obsession with Justin Bieber. In this talk, I will give an overview of some past and present research done at the Qatar Computing Research Institute (QCRI) which tries to find links between the online world and the offline world.

The first line of work looks at political tension in Egypt. Is it possible to quantify tension in a polarized society and maybe even predict outbreaks of violence? Based on our methodology we find evidence that monitoring the extreme poles can give indications about periods of violence.

Migration is one the major driving forces behind demographic changes around the world. In this second line of work we turn to online data and digital methods to see if we can quantify certain aspects of migration for a large number of countries and faster than typical reporting latencies of often more than a year.

A popular saying is that you are what you eat. We study if you also tweet what you eat and if it is possible to study food consumption using Twitter. Here, we are particularly interested in questions related to obesity and if there are "networks effects", but also in questions related to demographic influences such as income.

Bio:  Ingmar Weber is a senior scientist in the Social Computing Group at the Qatar Computing Research Institute (QCRI). He enjoys interdisciplinary research that uses "big data" and computer science methods to address research questions coming from other domains. His work focuses on how user-generated online data can be used to answer questions about society at large and the offline world in general. During his academic career he has gradually moved further South with stops at 52.2°N (Cambridge University), 49.2°N (Max-Planck Institute for Computer Science), 46.5°N (EPFL), 41.4°N (Yahoo! Research Barcelona) and 25.3°N (QCRI). Ingmar is co-organizer of the "Politics, Elections and Data" (PLEAD) workshop at CIKM 2012 and 2013, contributor to a WSDM 2013 tutorial on "Data-driven Political Science", co-editor of a Social Science Computing Review special issue on "Quantifying Politics Using Online Data", co-organizer of a CIKM 2013 tutorial on "Twitter and the Real World" and PC Co-Chair of SocInfo 2014. He has published more than 60 peer-reviewed articles and his research has been featured on Financial Times, New Scientist, Foreign Policy, Al Jazeera and other media. He loves chocolate, enjoys participating in the occasional ultra-marathon/triathlon and tweets at @ingmarweber.

The Origins of Common Sense: Modeling Human Intelligence with Probabilistic Programs and Program Induction
Friday, April 18, 2014 • 12:00PM-2:00PM
Computer Science Building, Room 150/151
Lunch will be provided, beginning at 12:00
Talk begins at 12:30

Abstract:  Our work seeks to understand the roots of human common-sense thought by looking at the core cognitive capacities and learning mechanisms of young children and infants. We build computational models of these capacities with the twin goals of explaining human thought in more principled, rigorous "reverse engineering" terms, and engineering more human-like AI and machine learning systems. This talk will focus on two ways in which the intelligence of very young children goes beyond existing machine systems: (1) Scene understanding, where we can detect not only objects and their locations, but what is happening, what will happen next, who is doing what to whom and why, in terms of our intuitive theories of physics (forces, masses) and psychology (beliefs, desires, ...); (2) Learning concepts from examples, where just a single example is often sufficient to grasp a new concept and generalize in richer ways than machine learning systems can typically do even with hundreds or thousands of examples. I will show how we are beginning to capture these reasoning and learning abilities in computational terms using techniques based on probabilistic programs and program induction, embedded in a broadly Bayesian framework for inference under uncertainty.

Bio:  Joshua B. Tenenbaum received his Ph.D. in 1999 from MIT in the Department of Brain and Cognitive Sciences, where he is currently Professor of Computational Cognitive Science as well as a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). He studies learning, reasoning and perception in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He and his collaborators have pioneered accounts of human cognition based on sophisticated probabilistic models, and have also developed several novel machine learning algorithms inspired by human learning. His papers have received awards at numerous conferences, including the IEEE Computer Vision and Pattern Recognition (CVPR) conference, Neural Information Processing Systems (NIPS), the Annual Meeting of the Cognitive Science Society, Uncertainty in AI (UAI), the International Joint Conference on Artificial Intelligence (IJCAI), and the International Conference on Development and Learning (ICDL). He is the recipient of early career awards from the Society for Mathematical Psychology, the Society of Experimental Psychologists, and the American Psychological Association, along with the Troland Research Award from the National Academy of Sciences. He is a fellow of the Society of Experimental Psychologists and the Cognitive Science Society.

Understanding Online Video Users: A Key to the Future of the Internet
Friday, April 25, 2014 • 12:00PM-2:00PM
Computer Science Building, Room 150/151
Lunch will be provided, beginning at 12:00
Talk begins at 12:30

Abstract:  Online video is the killer application of the Internet. It is predicted that more than 85% of the consumer traffic on the Internet will be video-related by 2016. But, can online videos ever be fully monetized? The future economic viability of online videos rest squarely on our ability to understand how viewers interact with video content. For instance: If a video fails to start up quickly, would the viewer abandon? If a video freezes in the middle, would the viewer watch fewer minutes of it? Where should video ads be inserted to ensure that they are watched to conclusion? Are ads in movies more likely to be watched than ads in short news clips? In this talk, we outline scientific answers to these and other such questions. The largest study of its kind, our work analyzes the video viewing habits of over 65 million unique users who in aggregate watched almost 367 million videos. To go beyond correlation and to establish causality, we develop a novel technique based on quasi-experimental designs (QEDs). While QEDs are well known in the medical and social sciences, our work represents its first use in network performance research and is of independent interest.

Bio:  Prof. Ramesh K. Sitaraman is currently in the School of Computer Science at the University of Massachusetts at Amherst. He is best known for his role in pioneering the first large content delivery networks (CDNs) that currently deliver a significant fraction of the world’s web content, streaming videos, and online applications. As a principal architect, he helped create Akamai's distributed network and is an Akamai Fellow. His research focuses on all aspects of Internet-scale distributed networks, including algorithms, architectures, performance, energy efficiency, user behavior, and economics. He received a B. Tech. in electrical engineering from the Indian Institute of Technology, Madras. and a Ph.D. in computer science from Princeton University.