Data-Driven Modeling and Analysis of Online Social Networks

  • Divyakant Agrawal
  • Bassam Bamieh
  • Ceren Budak
  • Amr El Abbadi
  • Andrew Flanagin
  • Stacy Patterson
Conference paper

DOI: 10.1007/978-3-642-23535-1_3

Volume 6897 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Agrawal D., Bamieh B., Budak C., El Abbadi A., Flanagin A., Patterson S. (2011) Data-Driven Modeling and Analysis of Online Social Networks. In: Wang H., Li S., Oyama S., Hu X., Qian T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg

Abstract

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 social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior 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 us to study social interactions on a scale and at a level of detail that has never before been possible.

We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (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 develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on real-world data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

Keywords

Information propagation Social Networks Data Analysis Sub-modular optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Divyakant Agrawal
    • 1
  • Bassam Bamieh
    • 2
  • Ceren Budak
    • 1
  • Amr El Abbadi
    • 1
  • Andrew Flanagin
    • 3
  • Stacy Patterson
    • 2
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of Mechanical EngineeringUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Department of CommunicationUniversity of CaliforniaSanta BarbaraUSA