{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:07:33Z","timestamp":1776078453682,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T00:00:00Z","timestamp":1429833600000},"content-version":"vor","delay-in-days":478,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc of Assoc for Info"],"published-print":{"date-parts":[[2014,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Social media is a vital source of information during any major event, especially natural disasters. However, with the exponential increase in volume of social media data, so comes the increase in conversational data that does not provide valuable information, especially in the context of disaster events, thus, diminishing peoples\u2019 ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. This project focuses on the development of a Bayesian approach to the classification of tweets (posts on Twitter) during Hurricane Sandy in order to distinguish \u201cinformational\u201d from \u201cconversational\u201d tweets. We designed an effective set of features and used them as input to Na\u00efve Bayes classifiers. In comparison to a \u201cbag of words\u201d approach, the new feature set provides similar results in the classification of tweets. However, the designed feature set contains only 9 features compared with more than 3000 features for \u201cbag of words.\u201d When the feature set is combined with \u201cbag of words\u201d, accuracy achieves 85.2914%. If integrated into disaster\u2010related systems, our approach can serve as a boon to any person or organization seeking to extract useful information in the midst of a natural disaster.<\/jats:p>","DOI":"10.1002\/meet.2014.14505101162","type":"journal-article","created":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T17:19:02Z","timestamp":1429895942000},"page":"1-4","source":"Crossref","is-referenced-by-count":29,"title":["Identifying valuable information from twitter during natural disasters"],"prefix":"10.1002","volume":"51","author":[{"given":"Brandon","family":"Truong","sequence":"first","affiliation":[{"name":"Computer Science and Engineering University of North Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cornelia","family":"Caragea","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering University of North Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Squicciarini","sequence":"additional","affiliation":[{"name":"Information Sciences and Technology Pennsylvania State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea H.","family":"Tapia","sequence":"additional","affiliation":[{"name":"Information Sciences and Technology Pennsylvania State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2015,4,24]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Alexander M.(2000).Confronting Catastrophe: New Perspectives on Natural Disasters."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v17i4.3937"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Bucher H.\u2010J.(2002).Crisis Communication and the Internet: Risk and Trust in a Global Media. First Monday.http:\/\/www.firstmonday.org\/issues\/issue7_4\/bucher\/","DOI":"10.5210\/fm.v7i4.943"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511617058","volume-title":"Satisficing & Maximizing Moral Theorists on Practical Reason","author":"Byron M.","year":"2004"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"Castillo C.;Mendoza M.; &Poblete B.Information credibility on twitter. In Proc. Of WWW pages675\u2013684 2011.","DOI":"10.1145\/1963405.1963500"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"Gao H.;Barbier G.; &GoolsbyR.\u201cHarnessing the crowdsourcing power of social media for disaster relief.\u201d IEEE Intelligent Systems 26.3 (2011):10\u201314.","DOI":"10.1109\/MIS.2011.52"},{"key":"e_1_2_8_8_1","unstructured":"Granger\u2010Happ E.The good enough principle \u2010 nonprofits. Retrieved fromwww.fairfieldreview.org\/\u2026\/TheGood Enough Principle\u2026 pages 1\u201325 2008."},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"Gundecha P; &Liu H.\u201cMining social media: A brief introduction.\u201d Tutorials in Operations Research 1.4 (2012).","DOI":"10.1287\/educ.1120.0105"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"Hui C.;Tyshchuk Y.;Wallace W.;Goldberg M.; &Magdon\u2010Ismail M.(2012).Information cascades in social media in response to a crisis: a preliminary model and a case study. 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In Proc. of CoNLL pages68\u201377 2011."},{"key":"e_1_2_8_20_1","unstructured":"McClendon S; &Robinson A. C.Leveraging geospatially\u2010oriented social media communications in disaster response. In Proceedings of the International ISCRAM Conference pages 2\u201311 2012."},{"key":"e_1_2_8_21_1","unstructured":"Qazvinian V.;Rosengren E.;Radev D. R.; &Mei Q.Rumor has it: Identifying misinformation in microblogs. In Proceedings of EMNLP EMNLP '11 pages 1589\u20131599 2011."},{"key":"e_1_2_8_22_1","doi-asserted-by":"crossref","unstructured":"Sakaki T;Okazaki M; &Matsuo Y.Earthquake shakes twitter users: Real\u2010time event detection by social sensors. In Proceedings of WWW pages 851\u2013860 2010.","DOI":"10.1145\/1772690.1772777"},{"key":"e_1_2_8_23_1","unstructured":"Starbird K.;Muzny G.; &Palen L.(2012).Learning from the crowd: Collaborative filtering techniques for identifying on\u2010the\u2010ground Twitterers during mass disruptions. Proc. 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