HAWKES BINOMIAL TOPIC MODEL WITH APPLICATIONS TO COUPLED CONFLICT-TWITTER DATA

• 成果类型: Article
• 署名作者: Mohler, George; McGrath, Erin; Buntain, Cody; LaFree, Gary
• 署名单位: Purdue University System; Purdue University; Purdue University in Indianapolis; University System of Maryland; University of Maryland College Park; New Jersey Institute of Technology; University System of Maryland; University of Maryland College Park
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1352
• 发表日期: 2020
• 页码: 1984-2002
• 关键词: crime

摘要

We consider the problem of modeling and clustering heterogeneous event data arising from coupled conflict event and social media data sets. In this setting conflict events trigger responses on social media, and, at the same time, signals of grievance detected in social media may serve as leading indicators for subsequent conflict events. For this purpose we introduce the Hawkes Binomial Topic Model (HBTM) where marks, Tweets and conflict event descriptions are represented as bags of words following a Binomial distribution. When viewed as a branching process, the daughter event bag of words is generated by randomly turning on/off parent words through independent Bernoulli random variables. We then use expectation-maximization to estimate the model parameters and branching structure of the process. The inferred branching structure is then used for topic cascade detection, short-term forecasting, and investigating the causal dependence of grievance on social media and conflict events in recent elections in Nigeria and Kenya.