Modeling topic trends on the social web using temporal signatures
Laura Christiansen, Thomas Schimoler, Robin Burke, Bamshad Mobasher · 2012
The Social Web makes visible the ebb and flow of popular interest in topics both newsworthy ("GulfSpill") and trivial ("Lolcat"). Understanding this emergent behavior is a fundamental goal for Social Web research. Key problems include discovering emergent topics from online text sources, modeling burst activity, and predicting the future trajectory of a given topic. Past work has addressed such problems individually for specific applications, but has lacked a generalizable framework for performing both classification and prediction of topic usage. Our approach is to model a topic as a temporally ordered sequence of derived feature states and capture characteristic changes in the topic trend. These sequences are drawn from a dynamic segmentation of frequency data based on change point analysis. We employ Partitioning Around Medoids clustering on these segments to produce signatures which highlight characteristic patterns of usage growth and decay. We demonstrate how this signature model can be used to define distinctive classes of topics in multiple online contexts, including tagging systems and web-based information retrieval. Additionally, we show how the model can predict the general trajectory of interest in a particular topic.