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Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90\u2009min and a sliding gap of 10\u2009min obtained the best F-measure value ( F\u2009=\u20090.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.<\/jats:p>","DOI":"10.1177\/0165551520979870","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T03:46:16Z","timestamp":1608608776000},"page":"718-732","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Using community information for natural disaster alerts"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-3010","authenticated-orcid":false,"given":"Chun Chieh","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Information Management, National Cheng Kung University"}]},{"given":"Hei-Chia","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Management, National Cheng Kung University"},{"name":"Center for Innovative FinTech Business Models, National Cheng Kung University"}]}],"member":"179","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"bibr1-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2011.52"},{"key":"bibr2-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2014.996567"},{"key":"bibr3-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1177\/0165551519828620"},{"key":"bibr4-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.04.020"},{"key":"bibr5-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2013.126"},{"key":"bibr6-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1007\/s13753-016-0086-5"},{"key":"bibr7-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.29"},{"key":"bibr8-0165551520979870","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-015-1835-2"},{"key":"bibr9-0165551520979870","unstructured":"Blake ES, Kimber lain TB, Berg RJ, et al. 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