NSF EAGER: Data-Driven Framework for Analyzing User Interactions in Social Media
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 diﬀusion over online so-
cial 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 pro ject
is 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.
The work proposed in this pro ject is high-risk since social networks and media are still evolving and
our understanding of such information ecosystem is still nascent. However, at the same time, this work
comes with a high-reward since a successful execution of this pro ject will essentially result in revolutionary
approaches for testing and validating human behaviors and interactions in a society at diﬀerent scales.
C. Budak, D. Agrawal, A. El Abbadi, "Limiting the spread of misinformation in social networks." , bibl. WWW11, pages 665-674, New York, NY, USA, 2011. ACM., (2011). Conference Published of Collection: , "Proceedings of the International Conference on World Wide Web"
C. Budak, D. Agrawal, and A. El Abbadi., "Structural trend analysis for online social networks." , bibl. Proc. VLDB Endow., 4:646-656, July 2011., (2011). Conference Published of Collection: , "International Conference on Very Large Data Bases"
Divyakant Agrawal, Bassam Bamieh, Ceren Budak, Amr El Abbadi, Andrew J. Flanagin, Stacy Patterson, "Data-Driven Modeling and Analysis of Online Social Networks" , bibl. ata-Driven Modeling and Analysis of Online Social Networks. WAIM 2011: 3-17, (2011). Conference Published of Collection: , "International Conference on Web-age Information Management"
Divyakant Agrawal, Ceren Budak, Amr El Abbadi, "Information Diffusion In Social Networks: Observing and Influencing Societal Interests." , bibl. PVLDB 4(12): 1512-1513 (2011), (2011). Conference Published of Collection: , "International Conference on Very Large Data Bases"
Divyakant Agrawal, Ceren Budak, Amr El Abbadi, "Information diffusion in social networks: Observing and affecting what society cares about." , bibl. CIKM 2011: 2609-2610, (2011). Conference Published of Collection: , "International Conference on Information and Knowledge Management"
Data Sets Used: