Twitter has become one of the most popular Location-Based Social Networks (LBSNs) that enables bridging physical and virtual worlds. Tweets, 140-character-long messages published in Twitter, are aimed to provide basic responses to the What's happening? question. Occurrences and events in the real life are usually reported through geo-located tweets by users on site. Uncovering event-related tweets from the rest is a challenging problem that necessarily requires exploiting different tweet features. With that in mind, we propose Tweet-SCAN, a novel event discovery technique based on the density-based clustering algorithm called DB-SCAN. Tweet-SCAN takes into account four main features from a tweet, namely content, time, location and user to cluster homogeneously event-related tweets. This new technique models textual content through a probabilistic topic model called Hierarchical Dirichlet Process and introduces Jensen-Shannon distance for the task of neighborhood identification in the textual dimension. As a matter of fact, we show Tweet-SCAN performance in a real data set of geo-located tweets posted during Barcelona local festivities in 2014, for which some of the events were known beforehand. By means of this data set, we are able to assess Tweet-SCAN capabilities to discover events, justify using a textual component and highlight the effects of several parameters.
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