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Tweet clustering for event detection is a powerful modern method to automatethe real-time detection of events. In this work we present a new tweetclustering approach, using a probabilistic approach to incorporate temporalinformation. By analysing the distribution of time gaps between tweets we showthat the gaps between pairs of related tweets exhibit exponential decay,whereas the gaps between unrelated tweets are approximately uniform. Guided bythis insight, we use probabilistic arguments to estimate the likelihood that apair of tweets are related, and build an improved clustering method. Our methodSocial Media Event Response Clustering (SMERC) creates clusters of tweets basedon their tendency to be related to a single event. We evaluate our method atthree levels: through traditional event prediction from tweet clustering, bymeasuring the improvement in quality of clusters created, and also comparingthe clustering precision and recall with other methods. By applying SMERC totweets collected during a number of sporting events, we demonstrate thatincorporating temporal information leads to state of the art clusteringperformance.

SMERC: Social media event response clustering using textual and temporal information