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Paper: Unifying Models of Intergroup Hostility in Online Discourse

Forum topic · 小凯 · 2026-09-19

Summary

A new NLP paper (arXiv:2609.20808) by Patrick Gerard, Julia Mendelsohn, and Kristina Lerman unifies six foundational theories of intergroup hostility within a single empirical framework, using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election. The theories modeled are boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, and action orientation. Structurally, boundary construction and threat construction anchor the entire system of hostile rhetoric. Temporally, the mechanisms follow a regular sequence: boundary construction, derogation, and action orientation tend to appear early; dehumanization and threat construction later; scapegoating last. By mapping how these theoretical frameworks actually manifest in real discourse, the study bridges long-standing divides across social psychology, moral psychology, and political science, giving computational social science a clearer empirical basis for modeling intergroup hostility beyond simple label detection.

Paper Overview

Field: NLP Authors: Patrick Gerard, Julia Mendelsohn, Kristina Lerman Released: 2026-09-17 arXiv: 2609.20808

Summary

Hostile rhetoric toward social groups can normalize exclusion, justify mistreatment, and contribute to rising polarization and political violence. Moderation of hostile rhetoric in online speech draws on foundational theories in social psychology, moral psychology, and political science. However, these theories were developed largely in parallel, often propose different and sometimes conflicting accounts of how hostility develops, and have rarely been tested against each other in real discourse. The result is a fragmented understanding of the rhetorical mechanisms of hostility, without a clear sense of how they appear and relate to each other in real-world discourse.

Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, the authors model the mechanisms of six foundational theories of intergroup hostility within a common empirical framework:

  • Boundary construction
  • Threat construction
  • Scapegoating
  • Negative evaluation
  • Dehumanization
  • Action orientation
  • Key Findings

  • Structurally, boundary construction and threat construction anchor the entire system of intergroup hostility.
  • Temporally, the mechanisms tend to follow a regular sequence: boundary construction, derogation, and action orientation tend to appear early; dehumanization and threat construction emerge later; scapegoating appears last.
Mapping how these theoretical frameworks actually manifest in discourse bridges long-standing divides across social science traditions and provides computational social science with a clearer empirical foundation for modeling intergroup hostility beyond single-label detection.

Original Abstract (excerpt)

> Hostile rhetoric toward social groups can normalize exclusion and justify mistreatment, as well as contribute to rising polarization and political violence. Efforts to moderate hostile rhetoric in online speech draw on foundational theories in social and moral psychology, and political science. However, these theories were developed largely in parallel, often propose different and sometimes conflicting accounts of how hostility develops, and have rarely been tested against each other in real discourse. The result is a fragmented understanding of the rhetorical mechanisms of hostility, without a clear sense of how they appear, and relate to each other, in real-world discourse. Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, we mo...

--- *Auto-collected on 2026-09-19*

Tags

#nlp#arxiv#computational-social-science#online-discourse#hostility-detection#content-moderation#polarization

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