{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:27:53Z","timestamp":1777854473869,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,3,5]],"date-time":"2018-03-05T00:00:00Z","timestamp":1520208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["R0126-16-1002"],"award-info":[{"award-number":["R0126-16-1002"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:p>An ability to predict people\u2019s interests in different regions would be valuable to many applications including marketing and policymaking. We posit that social media plays an important role in capturing collective user interests in different regions and their dynamics over time and across regions. Event mentions in microblogs of social media like Twitter not only reflect the people\u2019s interests in different regions but also affect the posting of future messages as the content of microblogs propagates to others through an online social network. Differentiating from the various network analysis techniques that have been developed to capture people\u2019s interests and their propagation patterns, we propose an event mention prediction method that utilises an analysis of inter-region relationships. We first obtain regional user interests for each topic by applying Latent Dirichlet Allocation (LDA) to region-specific collections of tweets and then compute pairwise similarities among regions. The resulting similarity-based region network becomes the basis for constructing region groups through Markov Cluster Algorithm, which helps removing noise relationships among regions. We then propose a relatively simple regression technique to predict future event mentions in different regions. We demonstrate that the proposed method outperforms the state-of-the-art event prediction method, confirming that the novel method of constructing groups from region-based sub-topic interests indeed contributes to the increase in the prediction accuracy.<\/jats:p>","DOI":"10.1177\/0165551518761012","type":"journal-article","created":{"date-parts":[[2018,3,5]],"date-time":"2018-03-05T04:11:56Z","timestamp":1520223116000},"page":"818-829","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting event mentions based on a semantic analysis of microblogs for inter-region relationships"],"prefix":"10.1177","volume":"44","author":[{"given":"Gwan","family":"Jang","sequence":"first","affiliation":[{"name":"School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Hyon","family":"Myaeng","sequence":"additional","affiliation":[{"name":"School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,5]]},"reference":[{"key":"bibr1-0165551518761012","doi-asserted-by":"publisher","DOI":"10.1145\/2876480.2876485"},{"key":"bibr2-0165551518761012","first-page":"8","volume":"5","author":"Arias M","year":"2013","journal-title":"ACM T Intel Syst Tec"},{"key":"bibr3-0165551518761012","first-page":"1387","volume-title":"Proceedings of the twenty-third international joint conference on artificial intelligence","author":"He J"},{"key":"bibr4-0165551518761012","first-page":"963","volume-title":"Proceedings of the 2015 SIAM international conference on data mining (SIAM)","author":"Zhao L"},{"key":"bibr5-0165551518761012","first-page":"1503","volume-title":"Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining","author":"Zhao L"},{"key":"bibr6-0165551518761012","first-page":"573","volume-title":"IEEE\/ACM international conference on proceedings of the advances in social networks analysis and mining (ASONAM)","author":"Jang G"},{"key":"bibr7-0165551518761012","first-page":"213","volume-title":"Proceedings of the first ACM conference on online social networks","author":"Ferrara E"},{"key":"bibr8-0165551518761012","first-page":"1104","volume-title":"Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining","author":"Ritter A"},{"key":"bibr9-0165551518761012","unstructured":"Weng J, Lee BS. Event detection in Twitter. 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