Predicting political conflicts from polarized social media

Abstract

In a polarized society, rhetorical arguments are usually expressed by strong, extreme terms which by themselves carry a positive or negative sentiment in favor or against one side of a social debate or conflict. In a divided society, by detecting extreme terms in a document such as a blog post which is reflecting some political opinion, we are potentially able to automatically detect the sentiment of the text about the polarizing issue. On the other hand, during any social and political conflict in a polarized society, we can observe a shift from mainstream to extreme language or rhetoric. In this paper, a new metric called “language gap” is introduced to estimate the distance between mainstream and rhetoric in a social-political debate. Then we illustrate that there is a correlation between the language shift and social conflicts. In other words, the language shift can be used as a signal for predicting social conflicts in a divided society.

Keywords

Social analytics social media conflicts polarized societies text mining sentiment analysis

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