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Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack

Burnap, Pete ORCID:, Williams, Matthew L. ORCID:, Sloan, Luke ORCID:, Rana, Omer ORCID:, Housley, William ORCID:, Edwards, Adam ORCID:, Knight, Vincent ORCID:, Procter, Rob and Voss, Alex 2014. Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Social Network Analysis and Mining 4 , 206. 10.1007/s13278-014-0206-4

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Little is currently known about the factors that promote the propagation of information in online social networks following terrorist events. In this paper we took the case of the terrorist event in Woolwich, London in 2013 and built models to predict information flow size and survival using data derived from the popular social networking site Twitter. We define information flows as the propagation over time of information posted to Twitter via the action of retweeting. Following a comparison with different predictive methods, and due to the distribution exhibited by our dependent size measure, we used the zero-truncated negative binomial (ZTNB) regression method. To model survival, the Cox regression technique was used because it estimates proportional hazard rates for independent measures. Following a principal component analysis to reduce the dimensionality of the data, social, temporal and content factors of the tweet were used as predictors in both models. Given the likely emotive reaction caused by the event, we emphasize the influence of emotive content on propagation in the discussion section. From a sample of Twitter data collected following the event (N=427,330) we report novel findings that identify that the sentiment expressed in the tweet is statistically significantly predictive of both size and survival of information flows of this nature. Furthermore, the number of offline press reports relating to the event published on the day the tweet was made was a significant predictor of size, as was the tension expressed in a tweet in relation to survival. Finally, time lags between retweets and the co-occurrence of URLS and Hashtags also emerged as significant.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Computer Science & Informatics
Social Sciences (Includes Criminology and Education)
Cardiff Centre for Crime, Law and Justice (CCLJ)
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HM Sociology
H Social Sciences > HT Communities. Classes. Races
Q Science > QA Mathematics > QA76 Computer software
Additional Information: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Publisher: Springer Verlag
ISSN: 1869-5450
Funders: ESRC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 23 May 2014
Last Modified: 23 Oct 2023 15:16

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