{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:16:25Z","timestamp":1778199385472,"version":"3.51.4"},"reference-count":16,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p>Nowadays, Twitter has become one of the fastest-growing microblogging services; consequently, analyzing this rich and continuously user-generated content can reveal unprecedentedly valuable knowledge. In this paper, we propose a novel two-stage system to detect and track events from tweets by integrating a Latent Dirichlet Allocation (LDA)-based approach and an efficient density\u2013contour-based spatio-temporal clustering approach. In the proposed system, we first divide the geotagged tweet stream into temporal time windows; next, events are identified as topics in tweets using an LDA-based topic discovery step; then, each tweet is assigned an event label; next, a density\u2013contour-based spatio-temporal clustering approach is employed to identify spatio-temporal event clusters. In our approach, topic continuity is established by calculating KL-divergences between topics and spatio-temporal continuity is established by a family of newly formulated spatial cluster distance functions. Moreover, the proposed density\u2013contour clustering approach considers two types of densities: \u201cabsolute\u201d density and \u201crelative\u201d density to identify event clusters where either there is a high density of event tweets or there is a high percentage of event tweets. We evaluate our approach using real-world data collected from Twitter, and the experimental results show that the proposed system can not only detect and track events effectively but also discover interesting patterns from geotagged tweets.<\/jats:p>","DOI":"10.1142\/s1793351x19400051","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T09:33:18Z","timestamp":1554283998000},"page":"87-110","source":"Crossref","is-referenced-by-count":12,"title":["Tracking Events in Twitter by Combining an LDA-Based Approach and a Density\u2013Contour Clustering Approach"],"prefix":"10.1142","volume":"13","author":[{"given":"Yongli","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Houston, Houston, TX 77204-3010, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph F.","family":"Eick","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Houston, Houston, TX 77204-3010, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,4,3]]},"reference":[{"key":"S1793351X19400051BIB003","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21149"},{"key":"S1793351X19400051BIB006","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2012.696123"},{"key":"S1793351X19400051BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0421-2"},{"key":"S1793351X19400051BIB011","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"issue":"6","key":"S1793351X19400051BIB012","first-page":"809","volume":"19","author":"Steiger E.","year":"2015","journal-title":"Trans. Geographic Information System"},{"key":"S1793351X19400051BIB013","first-page":"993","volume":"3","author":"Blei D. M.","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"S1793351X19400051BIB028","doi-asserted-by":"publisher","DOI":"10.1186\/s40064-015-0817-x"},{"key":"S1793351X19400051BIB033","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"S1793351X19400051BIB034","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780101112"},{"key":"S1793351X19400051BIB035","doi-asserted-by":"publisher","DOI":"10.2307\/3318678"},{"issue":"23","key":"S1793351X19400051BIB036","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1002\/sim.3995","volume":"29","author":"Davies T. M.","year":"2010","journal-title":"Stat. Med."},{"key":"S1793351X19400051BIB037","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2016.02.008"},{"key":"S1793351X19400051BIB038","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2009.09.003"},{"key":"S1793351X19400051BIB039","volume-title":"Clustering Algorithms","author":"Hartigan J. A.","year":"1975","edition":"90"},{"issue":"2","key":"S1793351X19400051BIB040","first-page":"1","volume":"1","author":"Cha S.-H.","year":"2007","journal-title":"City"},{"key":"S1793351X19400051BIB041","volume-title":"GESIS Data Archive, Dataset","author":"Pfeffer J.","year":"2016"}],"container-title":["International Journal of Semantic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793351X19400051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T11:32:31Z","timestamp":1607167951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793351X19400051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3]]},"references-count":16,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,4,3]]},"published-print":{"date-parts":[[2019,3]]}},"alternative-id":["10.1142\/S1793351X19400051"],"URL":"https:\/\/doi.org\/10.1142\/s1793351x19400051","relation":{},"ISSN":["1793-351X","1793-7108"],"issn-type":[{"value":"1793-351X","type":"print"},{"value":"1793-7108","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3]]}}}