When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models

ACL ARR 2025 February Submission4999 Authors

Abstract: Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven concepts evoked in immigration discourse (e.g. water or vermin). We propose and evaluate a novel technique that leverages both word-level and document-level signals to measure metaphor with respect to these concepts. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration. While conservatives tend to use dehumanizing metaphors more than liberals, this effect varies widely across concepts. Moreover, creature-related metaphor is associated with more retweets, especially for liberal authors. Our work highlights the potential for computational methods to complement qualitative approaches in understanding subtle and implicit language in political discourse.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: quantitative analyses of news and/or social media, frame detection and analysis, hate-speech detection, sociolinguistics, NLP tools for social analysis, metaphor

Contribution Types: Data resources, Data analysis, Theory

Languages Studied: English

Submission Number: 4999