Protest Event Analysis: A New Method Based on Twitter’s User Behaviors

Abstract

Protest Event Analysis is important for government officials and social scientists. Here we present a new method for predicting protest events and identifying indicators of protests and violence by monitoring the content generated on Twitter. By identifying these indicators, protests and the possibility of violence can be predicted and controlled more accurately. Twitter user behaviors such as opinion share and event log share are used as indicators and this study presents a new method based on a Bayesian logistic regression algorithm for predicting protests and violence using Twitter user behaviors. According to the proposed method, users’ event log share behaviors which include the rate of tweets containing date and time information is the reliable indicator for identifying protests. Users’ opinion share behaviors which include hate-anger tweet rates is also best for identifying violence in protests. A dataset which consists of tweets that are generated on protests in the Black Lives Matter (BLM) movement after the death of George Floyd is used in the evaluation of the proposed method. According to information published on acleddata.com, protests and violence have been reported in various cities on specific dates. The dataset contains 1414 protest events and 3078 non-protest events from 460 cities in 37 U.S. states. Protest events in the BLM movement between May 28 and June 30 among which 285 were violent and 1129 were peaceful. Our proposed method is tested on this dataset and the occurrence of protests is predicted with 85% precision. It is also possible to predict violence in protests with 85% precision with our method on this dataset. This study provides a successful method to predict small and large-scale protests, different from the existing literature focusing on large-scale protests.