Analyzing information flow in social networks for knowledge discovery

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Analyzing information flow in social networks for knowledge discovery

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In the last few years the online world has seen a surge in users’ social behavior. No longer is the image of a lone user surfing the web relevant anymore and with social sites such as Facebook, Twitter, etc. online users can now actively interact with other users. It is now quite common for web businesses to offer support for friends lists, forums, private message systems, community maintenance tools etc. As as result, not only are users finding more social satisfication online, but the businesses themselves are now able to interact with and monitor the communities around them. Consequently, large amounts of data are being collected from such “social systems”, which capture users’ participation in the community. The data can include user-user interactions as well as their activities with time stamps. The data is also unique in that it captures complex social phenomenon in a much more comprehensive manner and at a much more finer granularity, than any other traditional source of communication data. This presents rich opportunities for the development of knowledge discovery algorithms which will find immense value in revealing trends, latent structures or interesting behaviors in these social systems. In any social system, communication exposes people to information, opinions as well as behavior of other users. According to a well studied phenomenon in social science, summarized in the theory of contagion, users in such networks tend to develop beliefs, attitudes and assumptions that are similar to those of others around them. By “word-of-mouth” rumors, ideas, opinions, information, etc. can propagate to different regions in the network. The research presented in this thesis explores the analysis of such information flow in social networks from a variety of perspectives, including the network topology, actors’ interests, actors’ cognition and actors’ influence. It is shown that the proposed analyses techniques can discover valuable knowledge regarding community structure, user interests and sentiments, as well as prominent users in the community. Such knowledge is of immense value to online business owners, as it allows them to monitor and identify factors for improving the overall experience of their users.


University of Minnesota Ph.D. dissertation. February 2013. Major: Computer science. Advisors: Prof. Jaideep Srivastava & Prof. Arindam Banerjee. 1 computer file (PDF); ix, 117 pages.

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Pathak, Nishith. (2013). Analyzing information flow in social networks for knowledge discovery. Retrieved from the University Digital Conservancy,

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