Incremental clustering with vector expansion for online event detection in microblogs

Identifying similarities in microblog posts for event detection poses challenges due to short texts with idiosyncratic spellings, irregular writing styles, abbreviations and synonyms. In order to overcome these challenges, we present an enhancement to the incremental clustering techniques by detecting similar terms in microblog posts in a temporal context. We devise an unsupervised method to measure the similarities online using co-occurrence-based techniques and use them in a vector expansion process. The results of our evaluation performed on a tweet set indicate that the proposed vector expansion method helps identify similarities in tweets despite differences in their content. This facilitates the clustering of tweets and detection of events with higher accuracy without incurring a high execution cost.