Computer Science and Applied Mathematics

Processing Social Data
Devavrat Shah, Massachusetts Institute of Technology

An important byproduct of the emergence of social networking platforms is an access to abundance of social data in all forms: blogs, clicks, facebook feeds, transactions and tweets. It is of great interest to process this large volume of highly unstructured data to facilitate business decisions, public policy making or better social living. The key challenge lies in the fact that even though data is large in volume, the information content is very limited. Therefore, extracting meaningful answers has become a challenging computational and statistical task. In this talk, I will discuss how to resolve it successfully for important questions arising in the context of crowd-sourcing, ranking and viral advertising. The key to our success lies in the identification of the appropriate statistical framework inspired by human behavior for the problems at hand.

Background Review Articles:

Modeling Choice:
Appeared in NIPS 2008 and IEEE Transactions on Information Theory 2012
Web-link: arxiv.org/abs/0910.0895

Aggregating Wisdom of Crowd:
Appeared in NIPS 2011 and Operations Research (accepted to appear).
Web-link: arxiv.org/abs/1110.3564

Predicting Trends in Twitter:
Working paper
Web-link: arxiv.org/abs/1302.3639

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Computer Science and Applied Mathematics

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The Kavli Frontiers of Science symposium series is the National Academy of Science’s premiere activity for distinguished young scientists. Unlike meetings that focus on a narrow area of science, these meetings allow participants to explore innovative research ideas across a wide variety of fields and to develop new networks that will serve them as they progress in their careers..

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