2020/04/30 by Erik Scharwächter, Emmanuel Müller
Computer Science · Mathematics · Psychology · Social Sciences · #Artificial intelligence #Association (psychology) #Causal inference #Computer science #Data mining #Econometrics #Event (particle physics) #Hate Speech and Cyberbullying Detection #Inference #Machine learning #Mathematics #Media Influence and Politics #Point (geometry) #Psychology #Quantile #Series (stratigraphy) #Social media #Terrorism, Counterterrorism, and Political Violence #Time series #World Wide Web #cs.CY #cs.LG #stat.AP #stat.ME
paper · pdf · doi:10.1214/20-aoas1338
published as Annals of Applied Statistics 14 (3) 2020, pp. 1285-1303 · 19 pages, 8 figures, to appear in the Annals of Applied Statistics, source code available at https://github.com/diozaka/pECA
openalex created_date 2020/05/13 · arxiv created 2020/07/10 · openalex publication_date 2020/09/01 · arxiv updated 2020/10/01 · openalex updated_date 2026/08/05
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.