TwitterNews: Real time event detection from the Twitter data stream

Research in event detection from the Twitter streaming data has been gaining momentum in the last couple of years. Although such data is noisy and often contains misleading information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in incremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, in terms of the recall and the precision, using a publicly available corpus.