Logo image
Back
TUT: a statistical model for detecting trends, topics and user interests in social media
Conference proceeding

TUT: a statistical model for detecting trends, topics and user interests in social media

Xuning Tang and Christopher Yang
Proceedings of the 21st ACM international conference on information and knowledge management, pp 972-981
29 Oct 2012
DOI: https://doi.org/10.1145/2396761.2396884

Abstract

evolution modeling topic trend user interest
The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.

Metrics

14 Record Views
26 citations in Scopus

Details

Logo image