Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack
Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack
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DOI：
10.1007/s13278-014-0206-4
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发表时间：
2014-12-01
影响因子：
2.8
通讯作者：
Voss, Alex
中科院分区：
文献类型：
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作者：
Burnap, Pete;Williams, Matthew L.;Voss, Alex
恐怖事件发生后，促进在线社交网络信息传播的因素目前知之甚少。在本文中，我们以2013年发生在伦敦伍尔维奇的恐怖事件为例，使用流行的社交网站Twitter的数据建立了预测信息流大小和存活率的模型。我们将信息流定义为通过转发操作在Twitter上发布的信息随时间的传播。在对不同的预测方法进行比较之后，由于我们的依赖大小测量所表现出的分布，我们使用了零截断负二项(ZTNB)回归方法。为了对存活率进行建模，使用了COX回归技术，因为它估计了独立测量的比例风险比率。经过主成分分析以降低数据的维度，在两个模型中都使用了推文的社会、时间和内容因素作为预测因子。考虑到事件可能引起的情绪反应，我们在讨论部分强调情绪内容对传播的影响。从事件后收集的推特数据样本(N=427,330)，我们报告了新的发现，发现推文中表达的情绪在统计上显著预测这种性质的信息流的规模和生存。此外，在推文发布当天发表的与这一事件有关的线下新闻报道的数量是规模的重要预测因素，推文中表达的与生存有关的紧张局势也是如此。此外，转发之间的时间滞后以及URL和标签的同时出现也是显著的。
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 zerotruncated 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 cooccurrence of URLS and hashtags also emerged as significant.