论文标题
恐怖主义会引发在线仇恨言论吗？关于事件与时间序列的协会
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series
论文作者
论文摘要
仇恨言论在网络上无处不在。最近，导致在线仇恨言论的脱机原因受到了越来越多的关注。一个反复出现的问题是，极端事件的发生是否存在系统地触发在线仇恨言论的爆发，这是由仇恨的社交媒体帖子的高峰表明。正式地，这个问题转化为衡量稀疏事件系列与时间序列之间的关联。我们提出了一种新型的统计方法，用于测量，测试和可视化时间序列中罕见事件与峰之间的系统关联。与以前的因果推理或时间序列的独立测试相反，我们的方法仅着眼于事件和峰的时间安排，而没有其他分布特征。我们遵循最初为关联点过程而开发的事件重合分析（ECA）的框架。我们制定了ECA的离散时间变体，并得出所有必需的分布，以实现时间序列的峰值分析，特别关注多个阈值的串行依赖关系和峰值。分析通过分位数触发率图引起了对关联的新型可视化。我们通过分析西欧和北美的伊斯兰恐怖袭击是否系统地触发仇恨言论和反仇恨言论的伊斯兰恐怖袭击来证明我们的方法的实用性。
Hate speech is ubiquitous on the Web. Recently, the offline causes that contribute to online hate speech have received increasing attention. A recurring question is whether the occurrence of extreme events offline systematically triggers bursts of hate speech online, indicated by peaks in the volume of hateful social media posts. Formally, this question translates into measuring the association between a sparse event series and a time series. We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series. In contrast to previous methods for causal inference or independence tests on time series, our approach focuses only on the timing of events and peaks, and no other distributional characteristics. We follow the framework of event coincidence analysis (ECA) that was originally developed to correlate point processes. We formulate a discrete-time variant of ECA and derive all required distributions to enable analyses of peaks in time series, with a special focus on serial dependencies and peaks over multiple thresholds. The analysis gives rise to a novel visualization of the association via quantile-trigger rate plots. We demonstrate the utility of our approach by analyzing whether Islamist terrorist attacks in Western Europe and North America systematically trigger bursts of hate speech and counter-hate speech on Twitter.
