{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T21:24:02Z","timestamp":1777325042989,"version":"3.51.4"},"reference-count":17,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2014,11,11]],"date-time":"2014-11-11T00:00:00Z","timestamp":1415664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,11,11]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 This aim of this paper is to elucidate rumor propagation on microblogs and to assess a system for collecting rumor information to prevent rumor-spreading. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 We present a case study of how rumors spread on Twitter during a recent disaster situation, the Great East Japan earthquake of March 11, 2011, based on comparison to a normal situation. We specifically examine rumor disaffirmation because automatic rumor extraction is difficult. Extracting rumor-disaffirmation is easier than extracting the rumors themselves. We classify tweets in disaster situations, analyze tweets in disaster situations based on users' impressions and compare the spread of rumor tweets in a disaster situation to that in a normal situation. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The analysis results showed the following characteristics of rumors in a disaster situation. The information transmission is 74.9 per cent, representing the greatest number of tweets in our data set. Rumor tweets give users strong behavioral facilitation, make them feel negative and foment disorder. Rumors of a normal situation spread through many hierarchies but the rumors of disaster situations are two or three hierarchies, which means that the rumor spreading style differs in disaster situations and in normal situations. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The originality of this paper is to target rumors on Twitter and to analyze rumor characteristics by multiple aspects using not only rumor-tweets but also disaffirmation-tweets as an investigation object.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ijwis-04-2014-0015","type":"journal-article","created":{"date-parts":[[2014,11,16]],"date-time":"2014-11-16T13:19:46Z","timestamp":1416143986000},"page":"394-412","source":"Crossref","is-referenced-by-count":20,"title":["How do rumors spread during a crisis?"],"prefix":"10.1108","volume":"10","author":[{"given":"Mai","family":"Miyabe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akiyo","family":"Nadamoto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eiji","family":"Aramaki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122722532585500_b1","doi-asserted-by":"crossref","unstructured":"Abel, F.\n , \n Hauff, C.\n , \n Houben, G.J.\n , \n Stronkman, R.\n and \n Tao, K.\n (2012), \u201cTwitcident: fighting fire with information from social web streams\u201d, International Conference on World Wide Web (WWW), Lyon, France, pp. 305-308.","DOI":"10.1145\/2187980.2188035"},{"key":"key2020122722532585500_b2","doi-asserted-by":"crossref","unstructured":"Akamine, S.\n , \n Kawahara, D.\n , \n Kato, Y.\n , \n Nakagawa, T.\n , \n Inui, K.\n , \n Kurohashi, S.\n and \n Kidawara, Y.\n (2009), \u201cWISDOM: a web information credibility analysis system\u201d, Proceedings of the ACL-IJCNLP 2009 Software Demonstrations, Suntec, Singapore, pp. 1-4.","DOI":"10.3115\/1667872.1667873"},{"key":"key2020122722532585500_b3","unstructured":"Allport, G.W.\n and \n Postman, L.\n (1947), The Psychology of Rumor, Henry Holt, New York, NY."},{"key":"key2020122722532585500_b4","unstructured":"Aramaki, E.\n , \n Maskawa, S.\n and \n Morita, M.\n (2011), \u201cTwitter catches the flu: detecting influenza epidemics using Twitter\u201d, Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh, UK, pp. 1568-1576."},{"key":"key2020122722532585500_b5","doi-asserted-by":"crossref","unstructured":"Back, M.D.\n , \n Kufner, A.C.P.\n and \n Egloff, B.\n (2010), \u201cThe emotional timeline of September 11, 2001\u201d, Psychological Science, Sage Publications, United States, Vol. 21 No. 10, pp. 1417-1419.","DOI":"10.1177\/0956797610382124"},{"key":"key2020122722532585500_b6","doi-asserted-by":"crossref","unstructured":"Cohn, M.A.\n , \n Mehl, M.R.\n and \n Pennebaker, J.W.\n (2004), \u201cLinguistic markers of psychological change surrounding September 11, 2001\u201d, Psychological Science, Sage Publications, United States, Vol. 15 No. 10, pp. 687-693.","DOI":"10.1111\/j.0956-7976.2004.00741.x"},{"key":"key2020122722532585500_b7","doi-asserted-by":"crossref","unstructured":"Knapp, R.H.\n (1944), \u201cA psychology of rumor\u201d, Public Opinion Quarterly, American Association for Public Opinion Research, Deerfield, IL, Vol. 8 No. 1, pp. 22-37.","DOI":"10.1086\/265665"},{"key":"key2020122722532585500_b8","doi-asserted-by":"crossref","unstructured":"Longueville, B.D.\n , \n Smith, R.S.\n and \n Luraschi, G.\n (2009), \u201cOMG, from Here, I Can See the Flames!\u201d: a use case of mining location based social networks to acquire spatiotemporal data on forest fires\u201d, Proceedings of the 2009 International Workshop on Location Based Social Networks, Seattle, WA, USA, pp. 73-80.","DOI":"10.1145\/1629890.1629907"},{"key":"key2020122722532585500_b9","doi-asserted-by":"crossref","unstructured":"Mendoza, M.\n , \n Poblete, B.\n and \n Castillo, C.\n (2010), \u201cTwitter under crisis: can we trust what we RT?\u201d, Proceedings of First Workshop on Social Media Analysis (SOMA \u201810), Washington, DC, pp. 71-79.","DOI":"10.1145\/1964858.1964869"},{"key":"key2020122722532585500_b10","unstructured":"NECBIGLOBE\n (2011), \u201cUse of Twitter at the Great East Japan Earthquake\u201d, available at: www.biglobe.co.jp\/pressroom\/release\/2011\/04\/27-1"},{"key":"key2020122722532585500_b11","unstructured":"Paul, M.J.\n and \n Dredze, M.\n (2011), \u201cYou are what you tweet: analyzing Twitter for public health\u201d, Proceedings of Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), Barcelona, Catalonia, Spain, pp. 265-272."},{"key":"key2020122722532585500_b12","unstructured":"Qazvinian, V.\n , \n Rosengren, E.\n , \n Dragomir, R.R.\n and \n Mei, Q.\n (2011), \u201cRumor has it: identifying misinformation in microblogs\u201d, Proceedings of 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP\u20192011), Edinburgh, UK, pp. 1589-1599."},{"key":"key2020122722532585500_b14","doi-asserted-by":"crossref","unstructured":"Qu, Y.\n , \n Huang, C.\n , \n Zhang, P.\n and \n Zhang, J.\n (2011), \u201cMicroblogging after a major disaster in china: a case study of the 2010 Yushu earthquake\u201d, Proceedings of ACM 2011 Conference on Computer Supported Cooperative Work (CSCW \u201811), Hangzhou, China, pp. 25-34.","DOI":"10.1145\/1958824.1958830"},{"key":"key2020122722532585500_b13","unstructured":"Qu, Y.\n , \n Wu, P.F.\n and \n Wang, X.\n (2009), \u201cOnline community response to major disaster: a study of Tianya forum in the 2008 Sichuan earthquake\u201d, Proceedings of 42nd Hawaii International Conference on Systems Science (HICSS-42 2009), Big Island, Hawaii, pp. 1-11."},{"key":"key2020122722532585500_b15","doi-asserted-by":"crossref","unstructured":"Rosnow, R.L.\n , \n Esposito, J.L.\n and \n Gibney, L.\n (1988), \u201cFactors influencing rumor spreading: replication and extension\u201d, Language and Communication, Elsevier, Amsterdam, Vol. 8 No. 1, pp. 29-42.","DOI":"10.1016\/0271-5309(88)90004-3"},{"key":"key2020122722532585500_b16","doi-asserted-by":"crossref","unstructured":"Sakaki, M.\n , \n Okazaki, Y.\n and \n Matsuo, Y.\n (2010), \u201cEarthquake shakes Twitter users: real-time event detection by social sensors\u201d, Proceedings of International Conference on World Wide Web (WWW), Raleigh, NC, USA, pp. 851-860.","DOI":"10.1145\/1772690.1772777"},{"key":"key2020122722532585500_b17","doi-asserted-by":"crossref","unstructured":"Vieweg, S.\n , \n Hughes, A.L.\n , \n Starbird, K.\n and \n Palen, L.\n (2010), \u201cMicroblogging during two natural hazards events: what Twitter may contribute to situational awareness\u201d, Proceedings of 28th International Conference on Human Factors in Computing Systems (CHI \u201810), Atlanta, GA, USA, pp. 1079-1088.","DOI":"10.1145\/1753326.1753486"}],"container-title":["International Journal of Web Information 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Japan"],"editor":[{"given":"Maria","family":"Indrawan-Santiago","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2014,11,11]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,11,11]]}},"alternative-id":["10.1108\/IJWIS-04-2014-0015"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-04-2014-0015","relation":{},"ISSN":["1744-0084"],"issn-type":[{"value":"1744-0084","type":"print"}],"subject":[],"published":{"date-parts":[[2014,11,11]]}}}