Exploiting real-time information retrieval in the microblogosphere

Information seeking behavior in microblogging environments such as Twitter differs from traditional web search. The best performing microblog retrieval techniques attempt to utilize both semantic and temporal aspects of documents. In this paper, we present an effective approach, including the query modeling, the document modeling and the temporal re-ranking, to discover the most recent but relevant information to the query. For the query modeling, we introduce a two-stage pseudo-relevance feedback query expansion to overcome the severe vocabulary-mismatch problem of short message retrieval in microblog. For the document modeling, we propose two ways to expand document with the help of the shortened URL. For the temporal re-ranking, we suggest several methods to evaluate the temporal aspects of documents. Experimental results demonstrate that our approach obtains significant improvements compared with baseline systems. Specifically, the proposed system gives 26.37% and 9.94% further increases in email protected and MAP over the best performing result on highrel in the TREC'11 Real-Time Search Task.