Hawkes binomial topic model with applications to coupled conflict-twitter data

Document Type

Article

Publication Date

1-1-2020

Abstract

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 indica-tors for subsequent conflict events. For this purpose we introduce the Hawkes Binomial Topic Model (HBTM) where marks, Tweets and conflict event de-scriptions are represented as bags of words following a Binomial distribu-tion. 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 esti-mate 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.

Identifier

85098243536 (Scopus)

Publication Title

Annals of Applied Statistics

External Full Text Location

https://doi.org/10.1214/20-AOAS1352

e-ISSN

19417330

ISSN

19326157

First Page

1984

Last Page

2002

Issue

4

Volume

14

Grant

1737996

Fund Ref

National Science Foundation

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