The Journal of Machine Learning Research
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In recent times, microblogging sites like Facebook and Twitter have gained a lot of popularity. Millions of users world wide have been using these sites to post content that interests them and also to voice their opinions on several current events. In this paper, we present a novel non-parametric probabilistic model - Temporally driven Theme Event Model (TEM) for analyzing the content on microblogs. We also describe an online inference procedure for this model that enables its usage on large scale data. Experimentation carried out on real world data extracted from Facebook and Twitter demonstrates the efficacy of the proposed approach.