Open Access
December 2020 Hawkes binomial topic model with applications to coupled conflict-Twitter data
George Mohler, Erin McGrath, Cody Buntain, Gary LaFree
Ann. Appl. Stat. 14(4): 1984-2002 (December 2020). DOI: 10.1214/20-AOAS1352


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.


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George Mohler. Erin McGrath. Cody Buntain. Gary LaFree. "Hawkes binomial topic model with applications to coupled conflict-Twitter data." Ann. Appl. Stat. 14 (4) 1984 - 2002, December 2020.


Received: 1 September 2018; Revised: 1 February 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194257
Digital Object Identifier: 10.1214/20-AOAS1352

Keywords: dynamic topic model , elections , Hawkes process , social media

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.14 • No. 4 • December 2020
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