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

2026-02-09

AI Summary

This study investigates the psychological mechanism of identity fusion—its linguistic manifestations, cross-cultural expressions, and divergent pathways toward violence. To this end, the research pioneers an integrative computational framework that combines cognitive linguistic theory with large language models (LLMs), leveraging metaphor analysis and natural language processing to quantify identity fusion in textual data. Evaluated on datasets from the UK and Singapore, the proposed method significantly outperforms existing models. It successfully identifies two distinct subtypes of highly fused individuals within extremist manifestos: ideology-driven and grievance-driven, each associated with unique linguistic patterns. The findings offer a novel theoretical lens and empirical toolkit for understanding the mechanisms underlying radicalization.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Language and Vision
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• identity fusion
• extremism
• language
• violence
• polarization

Innovation

• identity fusion
• cognitive linguistics
• large language models
• extremism detection
• implicit metaphor