Topic Detection via Participation Using Markov Logic Network

Abstract

The advent of Web 2.0 enables the proliferation of online communities in which tremendous number of Internet users contribute and share enormous information. Proper exploitation of community structure help retrieving useful information and better understanding of their features. We employ Markov Logic Network to explore topic tracking by finding clusters, which represents latent topics, best fitting a set of rules. Rather than using contents in investigating discussions of a community, the user participation is used because it is believed that topics can be somehow reflected by the preferences of participation. User participation is also easier to process than text. The clustering results show this approach can reveal latent topics of a community effectively.