A temporal Microblog filtering model

The rapid growth in the popularity of social networking and microblogging has led to a new way of finding and broadcasting information in the recent years. The real-time microblog filtering emerges as the times require. The task of real-time microblog filtering is to decide if subsequently posted tweets are relevant to a given query which represents the special information needs. One-side feedback is one of the most difficult problems in microblog filtering. This paper focuses on exploiting the time profile of relevant microblogs to address this problem. A temporal microblog filtering based on retrieval model is proposed. Specifically, similarity threshold achieved by the language model is adjusted according to temporal burst. Evaluated on the TREC 2012 microblog real-time filtering track dataset, the experimental results show that the proposed model is significantly better than several baselines.