Applying Text Mining to Protest Stories as Voice against Media Censorship

TL;DR

This paper explores how analyzing personal protest stories through text mining techniques can provide valuable insights into activism and protests, especially under media censorship conditions.

Contribution

It introduces a method to analyze protest stories by extracting location networks and performing emotion mining, offering an alternative data source for activism analysis.

Findings

Location networks reveal protest spread patterns

Emotion mining uncovers underlying sentiments

Personal stories serve as a voice during censorship

Abstract

Data driven activism attempts to collect, analyze and visualize data to foster social change. However, during media censorship it is often impossible to collect such data. Here we demonstrate that data from personal stories can also help us to gain insights about protests and activism which can work as a voice for the activists. We analyze protest story data by extracting location network from the stories and perform emotion mining to get insight about the protest.