Data-Driven Framework for Analyzing User Interactions in Social Media

High level project in Social Networks (relating to various other DSL projects) involving collaborations of: Divy Agrawal (Computer Science), Stacy Patterson (Mechanical Engineering), Andrew Flanagin (Communication), Bassam Bamieh (Mechanical Engineering), Amr El Abbadi (Computer Science)

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diļ¬€usion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, social scientists have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, these investigations required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow social scientists to study social interactions on a scale and at a level of detail that has never before been possible. Research in social network analysis, however, requires inter-disciplinary collaboration.
This proposal presents an integrated research approach for analyzing user interactions in online social networks that combines expertise in three key areas: (1) Collection, Querying, and analysis of massive datasets (2) Modeling and analysis of complex networks (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to evaluate and validate existing models of social processes, and ultimately to create a shared data repository and a collection of evaluation metrics that can be used broadly by other research investigations in social networks.

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