NLP Case Study on Predicting the Before and After of the Ukraine-Russia and Hamas-Israel Conflicts

TL;DR

This study uses NLP techniques on social media data from Twitter and Reddit to predict conflict-related toxicity and discourse changes before and after the Ukraine-Russia and Hamas-Israel conflicts, aiming to mitigate risks.

Contribution

It introduces a method combining supervised and unsupervised NLP techniques to predict social media discourse changes related to conflicts with high accuracy.

Findings

Social media discussions differ significantly before and after conflicts.

NLP models can predict conflict-related discourse with approximately 1.2% error.

Social media analysis can potentially forecast future conflicts.

Abstract

We propose a method to predict toxicity and other textual attributes through the use of natural language processing (NLP) techniques for two recent events: the Ukraine-Russia and Hamas-Israel conflicts. This article provides a basis for exploration in future conflicts with hopes to mitigate risk through the analysis of social media before and after a conflict begins. Our work compiles several datasets from Twitter and Reddit for both conflicts in a before and after separation with an aim of predicting a future state of social media for avoidance. More specifically, we show that: (1) there is a noticeable difference in social media discussion leading up to and following a conflict and (2) social media discourse on platforms like Twitter and Reddit is useful in identifying future conflicts before they arise. Our results show that through the use of advanced NLP techniques (both supervised and unsupervised) toxicity and other attributes about language before and after a conflict is predictable with a low error of nearly 1.2 percent for both conflicts.