{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:51:52Z","timestamp":1762955512458},"reference-count":34,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2016,2,22]],"date-time":"2016-02-22T00:00:00Z","timestamp":1456099200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Explicit references on Twitter to future events can be leveraged to feed a fully automatic monitoring system of real-world events. We describe a system that extracts open-domain future events from the Twitter stream. It detects future time expressions and entity mentions in tweets, clusters tweets together that overlap in these mentions above certain thresholds, and summarizes these clusters into event descriptions that can be presented to users of the system. Terms for the event description are selected in an unsupervised fashion.<jats:sup>1<\/jats:sup>We evaluated the system on a month of Dutch tweets, by showing the top-250 ranked events found in this month to human annotators. Eighty per cent of the candidate events were indeed assessed as being an event by at least three out of four human annotators, while all four annotators regarded sixty-three per cent as a real event. An added component to complement event descriptions with additional terms was not assessed better than the original system, due to the occasional addition of redundant terms. Comparing the found events to gold-standard events from maintained calendars on the Web mentioned in at least five tweets, the system yields a recall-at-250 of 0.20 and a recall based on all retrieved events of 0.40.<\/jats:p>","DOI":"10.1017\/s1351324916000036","type":"journal-article","created":{"date-parts":[[2016,2,22]],"date-time":"2016-02-22T12:23:40Z","timestamp":1456143820000},"page":"655-686","source":"Crossref","is-referenced-by-count":16,"title":["Open-domain extraction of future events from Twitter"],"prefix":"10.1017","volume":"22","author":[{"given":"FLORIAN","family":"KUNNEMAN","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ANTAL","family":"VAN DEN BOSCH","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2016,2,22]]},"reference":[{"key":"S1351324916000036_ref032","first-page":"401","volume-title":"Proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM-11)","author":"Weng","year":"2011"},{"key":"S1351324916000036_ref021","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1971.10482356"},{"key":"S1351324916000036_ref028","first-page":"121","article-title":"Dealing with big data: the case of Twitter","volume":"3","author":"Tjong Kim Sang","year":"2013","journal-title":"Computational Linguistics in the Netherlands Journal"},{"key":"S1351324916000036_ref023","first-page":"1524","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Ritter","year":"2011"},{"key":"S1351324916000036_ref003","first-page":"389","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies","author":"Benson","year":"2011"},{"key":"S1351324916000036_ref017","volume-title":"Computer-Intensive Methods for Testing Hypotheses: An Introduction","author":"Noreen","year":"1989"},{"key":"S1351324916000036_ref007","doi-asserted-by":"publisher","DOI":"10.1007\/BF01890115"},{"key":"S1351324916000036_ref020","first-page":"181","volume-title":"Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics","author":"Petrovi\u0107","year":"2010"},{"key":"S1351324916000036_ref013","first-page":"26","volume-title":"Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)","author":"Kunneman","year":"2014"},{"key":"S1351324916000036_ref034","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20161-5_34"},{"key":"S1351324916000036_ref025","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1145\/1772690.1772777","volume-title":"Proceedings of the 19th International Conference on World Wide Web","author":"Sakaki","year":"2010"},{"key":"S1351324916000036_ref022","volume-title":"Proceedings of the 2nd ACM International Conference on Multimedia Retrieval","author":"Reuter","year":"2012"},{"key":"S1351324916000036_ref027","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488499"},{"key":"S1351324916000036_ref024","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339704"},{"key":"S1351324916000036_ref010","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012801612483"},{"key":"S1351324916000036_ref033","unstructured":"Zhao S. , Zhong L. , Wickramasuriya J. , and Vasudevan V. 2011. Human as real-time sensors of social and physical events: a case study of Twitter and sports games. Technical Report TR0620-2011, Houston, TX: Rice University and Motorola Labs."},{"key":"S1351324916000036_ref008","first-page":"536","volume-title":"Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1","author":"Diao","year":"2012"},{"key":"S1351324916000036_ref031","first-page":"1","volume-title":"Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks","author":"Weiler","year":"2013"},{"key":"S1351324916000036_ref026","first-page":"321","volume-title":"Proceedings of the 5th International Workshop on Semantic Evaluation","author":"Str\u00f6tgen","year":"2010"},{"key":"S1351324916000036_ref012","unstructured":"Kumar S. , Liu H. , Mehta S. , and Venkata Subramaniam L. 2014. 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