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.