Discovery of Trending Topics in Microblog Streams Based on Contextual Search

With the rapid growing of microblog, the need to discover the trending topics in microblogs becomes more and more pressing. Although topic detection has long been a hotspot research, it is difficult to discover the trending topics with high contextual meaning because the microblogs are small elements of content, such as short sentences, individual images, or video links. This paper proposes a novel approach to detect trending topics in microblog streams based on contextual search. First, burst keywords from microblog segment will be detected by calculating their frequencies and average growth rates; then related tweets are clustered as a document based on a contextual search method; finally, top-k keywords in the document are regarded as the trending topic based on a TF-IDF method. The experiment results show that the proposed approach can detect trending topics with high contextual meaning and accuracy.