Behavior analysis based protest event detection

Purpose: Social unrest is a phenomenon that occurs in all countries, both developed and poor. The only difference is in the cause of a social unrest and it is mostly economic in underdeveloped countries. The occurrence of protest and the role of social networks in it have always been debatable topics among researchers. 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. Methods: 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 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. Results: A research database consists of tweets generated on the BLM (Black Lives Matter) movement after the death of George Floyd. 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 include 1414 protests in the BLM movement between May 28 and June 30 among which 285 were violent and 1129 were peaceful. We tested our proposed method 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. Conclusion: According to the research findings, the behavior of users on the Twitter social network is a reliable source for predicting incidents and violence. This study provides a successful method to predict small and large-scale protests, different from the existing literature focusing on large-scale protests. Keywords: Protest Detection, Event Detection, Social Behavior, Social Media, Bayesian Logistic Regression, Machine Learning