Language Predicts Identity Fusion Across Cultures and Reveals Divergent Pathways to Violence

TL;DR

This paper introduces a novel language-based method to measure identity fusion, revealing different pathways to violence across cultures and enhancing extremism prediction tools.

Contribution

It develops the Cognitive Linguistic Identity Fusion Score using language models and metaphor analysis, outperforming existing measures and uncovering cultural differences in extremism pathways.

Findings

Language predicts identity fusion across cultures.

Two distinct pathways to violence identified: kinship vs. personal grievance.

New scalable tool for extremism detection.

Abstract

In light of increasing polarization and political violence, understanding the psychological roots of extremism is increasingly important. Prior research shows that identity fusion predicts willingness to engage in extreme acts. We evaluate the Cognitive Linguistic Identity Fusion Score, a method that uses cognitive linguistic patterns, LLMs, and implicit metaphor to measure fusion from language. Across datasets from the United Kingdom and Singapore, this approach outperforms existing methods in predicting validated fusion scores. Applied to extremist manifestos, two distinct high-fusion pathways to violence emerge: ideologues tend to frame themselves in terms of group, forming kinship bonds; whereas grievance-driven individuals frame the group in terms of their personal identity. These results refine theories of identity fusion and provide a scalable tool aiding fusion research and extremism detection.