Fear and Loathing on the Frontline: Decoding the Language of Othering by Russia-Ukraine War Bloggers

TL;DR

This paper introduces a novel computational framework using large language models to quantify and analyze the dynamics of othering in online war-related discourse, revealing how it escalates during conflicts and interacts with moral language.

Contribution

The paper presents a new LLM-based method to detect and analyze othering in diverse online contexts, extending beyond traditional hate speech detection.

Findings

Othering escalates during conflicts and crises.

Interaction between othering and moral language increases attention.

Framework effectively captures nuanced othering dynamics.

Abstract

Othering, the act of portraying outgroups as fundamentally different from the ingroup, often escalates into framing them as existential threats--fueling intergroup conflict and justifying exclusion and violence. These dynamics are alarmingly pervasive, spanning from the extreme historical examples of genocides against minorities in Germany and Rwanda to the ongoing violence and rhetoric targeting migrants in the US and Europe. While concepts like hate speech and fear speech have been explored in existing literature, they capture only part of this broader and more nuanced dynamic which can often be harder to detect, particularly in online speech and propaganda. To address this challenge, we introduce a novel computational framework that leverages large language models (LLMs) to quantify othering across diverse contexts, extending beyond traditional linguistic indicators of hostility. Applying the model to real-world data from Telegram war bloggers and political discussions on Gab reveals how othering escalates during conflicts, interacts with moral language, and garners significant attention, particularly during periods of crisis. Our framework, designed to offer deeper insights into othering dynamics, combines with a rapid adaptation process to provide essential tools for mitigating othering's adverse impacts on social cohesion.