{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T18:42:04Z","timestamp":1751827324006,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/978-1-61499-578-4-110","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":2,"title":["Tweet-SCAN: An event discovery technique for geo-located tweets"],"prefix":"10.3233","author":[{"family":"Capdevila Joan","sequence":"additional","affiliation":[]},{"family":"Cerquides Jes&uacute;s","sequence":"additional","affiliation":[]},{"family":"Nin Jordi","sequence":"additional","affiliation":[]},{"family":"Torres Jordi","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:49:28Z","timestamp":1740134968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-577-7&spage=110&doi=10.3233\/978-1-61499-578-4-110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-578-4-110","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}