  We propose an automated and unsupervised methodology for a novel
summarization of group behavior based on content preference. We show that graph
theoretical community evolution (based on similarity of user preference for
content) is effective in indexing these dynamics. Combined with text analysis
that targets automatically-identified representative content for each
community, our method produces a novel multi-layered representation of evolving
group behavior. We demonstrate this methodology in the context of political
discourse on a social news site with data that spans more than four years and
find coexisting political leanings over extended periods and a disruptive
external event that lead to a significant reorganization of existing patterns.
Finally, where there exists no ground truth, we propose a new evaluation
approach by using entropy measures as evidence of coherence along the evolution
path of these groups. This methodology is valuable to designers and managers of
online forums in need of granular analytics of user activity, as well as to
researchers in social and political sciences who wish to extend their inquiries
to large-scale data available on the web.
