Burnap, Pete, Williams, Matthew L., Sloan, Luke, Rana, Omer, Housley, William, Edwards, Adam, Knight, Vincent, Procter, Rob and Voss, Alex (2014) Tweeting the terror : modelling the social media reaction to the Woolwich terrorist attack. Social Network Analysis and Mining, Volume 4 (Number 1). Article number 206. doi:10.1007/s13278-014-0206-4 ISSN 1869-5450 .

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Abstract

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 posted was a significant predictor of size, as was the tension expressed in a tweet in relation to survival. Furthermore, time lags between retweets and the co-occurrence of URLS and hashtags also emerged as significant.

Item Type:	Journal Article
Subjects:	H Social Sciences > HM Sociology
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Social Sciences > Sociology
Library of Congress Subject Headings (LCSH):	Online social networks, Twitter, Terrorism -- England -- London, Social sciences -- Network analysis
Journal or Publication Title:	Social Network Analysis and Mining
Publisher:	Springer Wien
ISSN:	1869-5450
Official Date:	June 2014
Dates:	Date Event June 2014 Published 13 June 2014 Available 23 May 2014 Accepted 16 February 2014 Submitted
Volume:	Volume 4
Number:	Number 1
Number of Pages:	14
Article Number:	Article number 206
DOI:	10.1007/s13278-014-0206-4
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	29 December 2015
Date of first compliant Open Access:	29 December 2015
Funder:	Economic and Social Research Council (Great Britain) (ESRC)
Embodied As:	1

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