恐怖主义会引发网络仇恨言论吗？关于事件与时间序列的关联
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series
摘要：
仇恨言论在网络上无处不在。最近，导致网上仇恨言论的线下原因越来越受到关注。一个反复出现的问题是，线下极端事件的发生是否系统地引发了网上仇恨言论的爆发，这表现在仇恨社交媒体帖子数量的峰值。从形式上讲，这个问题转化为测量稀疏事件序列和时间序列之间的关联。我们提出了一种新的统计方法学来测量，测试和可视化的系统之间的关联罕见的事件和峰值在时间序列。与以往的时间序列的因果推断或独立性检验方法相比，我们的方法只关注事件和峰值的时间，而没有其他分布特征。我们遵循事件重合分析（ECA），最初开发的相关点过程的框架。我们制定了一个离散时间变量的ECA和推导出所有所需的分布，使时间序列的峰值分析，特别关注序列依赖性和峰值超过多个阈值。分析产生了一种新的可视化的关联，通过分位数触发率图。我们通过分析西欧和北美的伊斯兰恐怖袭击是否会系统地引发Twitter上的仇恨言论和反仇恨言论来证明我们的方法的实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
