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Predicting Tweet Retweetability during Hurricane Disasters

Predicting Tweet Retweetability during Hurricane Disasters

Venkata Kishore Neppalli, Cornelia Caragea, Doina Caragea, Murilo Cerqueira Medeiros, Andrea H. Tapia, Shane E. Halse
Copyright: © 2016 |Volume: 8 |Issue: 3 |Pages: 19
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781466690509|DOI: 10.4018/IJISCRAM.2016070103
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MLA

Neppalli, Venkata Kishore, et al. "Predicting Tweet Retweetability during Hurricane Disasters." IJISCRAM vol.8, no.3 2016: pp.32-50. http://doi.org/10.4018/IJISCRAM.2016070103

APA

Neppalli, V. K., Caragea, C., Caragea, D., Medeiros, M. C., Tapia, A. H., & Halse, S. E. (2016). Predicting Tweet Retweetability during Hurricane Disasters. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 8(3), 32-50. http://doi.org/10.4018/IJISCRAM.2016070103

Chicago

Neppalli, Venkata Kishore, et al. "Predicting Tweet Retweetability during Hurricane Disasters," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 8, no.3: 32-50. http://doi.org/10.4018/IJISCRAM.2016070103

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Abstract

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 “tweets,” 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.

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