{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T16:39:05Z","timestamp":1718901545658},"reference-count":39,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<p>Twitter is a vital source for obtaining information, especially during events such as natural disasters. Users can spread information on Twitter either by crafting new posts, which are called \u201ctweets,\u201d or by using the retweet mechanism to re-post previously created tweets. During natural disasters, identifying how likely a tweet is to be retweeted is crucial since it can help promote the spread of useful information in a social network such as Twitter, as well as it can help stop the spread of misinformation when corroborated with approaches that identify rumors and misinformation. In this paper, we present an analysis of retweeted tweets from two different hurricane disasters, to identify factors that affect retweetability. We then use these factors to extract features from tweets' content and user account information in order to develop models that automatically predict the retweetability of a tweet. The results of our experiments on Sandy and Patricia Hurricanes show the effectiveness of our features.<\/p>","DOI":"10.4018\/ijiscram.2016070103","type":"journal-article","created":{"date-parts":[[2017,3,24]],"date-time":"2017-03-24T13:46:51Z","timestamp":1490363211000},"page":"32-50","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Tweet Retweetability during Hurricane Disasters"],"prefix":"10.4018","volume":"8","author":[{"given":"Venkata Kishore","family":"Neppalli","sequence":"first","affiliation":[{"name":"University of North Texas, Computer Science and Engineering, Denton, TX, USA"}]},{"given":"Cornelia","family":"Caragea","sequence":"additional","affiliation":[{"name":"University of North Texas, Computer Science and Engineering, Denton, TX, USA"}]},{"given":"Doina","family":"Caragea","sequence":"additional","affiliation":[{"name":"Kansas State University, Department of Computer Science, Manhattan, KS, USA"}]},{"given":"Murilo Cerqueira","family":"Medeiros","sequence":"additional","affiliation":[{"name":"University of North Texas, Computer Science and Engineering, Denton, TX, USA"}]},{"given":"Andrea H","family":"Tapia","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, University Park, PA, USA"}]},{"given":"Shane E.","family":"Halse","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, University Park, PA, USA"}]}],"member":"2432","reference":[{"key":"IJISCRAM.2016070103-0","first-page":"21","article-title":"Design and Implementation of a Data Analytics Infrastructure In Support of Crisis Informatics Research.","volume":"\u201811","author":"K.Anderson","year":"2011","journal-title":"ICSE"},{"key":"IJISCRAM.2016070103-1","first-page":"69","article-title":"Metrics for Understanding Communication on Twitter","author":"A.Bruns","year":"2014","journal-title":"Twitter and Society"},{"key":"IJISCRAM.2016070103-2","doi-asserted-by":"crossref","unstructured":"Cameron, M. 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