Blind Men and the Elephant: Detecting Evolving Groups In Social News
This work provides granular analytics for designers and managers of online forums and researchers in social and political sciences, but it is incremental as it builds on existing graph and text analysis methods.
The authors tackled the problem of detecting evolving groups in social news by proposing an unsupervised methodology that combines graph-based community evolution and text analysis to create a multi-layered representation of group behavior, demonstrating it on political discourse data over four years and finding coexisting political leanings and disruptive events that reorganize patterns.
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.