# A Framework for the Computational Linguistic Analysis of Dehumanization

arXiv: https://arxiv.org/abs/2003.03014 · HTML: https://mlsanity.com/paper/2003.03014/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 30 of 31)

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- [DefVerify: Do Hate Speech Models Reflect Their Dataset's Definition?](https://mlsanity.com/paper/2410.15911/) (2024)
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- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2208.01312/) (2022)
- [A Dataset for the Detection of Dehumanizing Language](https://mlsanity.com/paper/2402.08764/) (2024)
- [Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models](https://mlsanity.com/paper/2408.07873/) (2024)
- [Riveter: Measuring Power and Social Dynamics Between Entities](https://mlsanity.com/paper/2312.09536/) (2023)
- [Beyond Hate Speech: NLP's Challenges and Opportunities in Uncovering Dehumanizing Language](https://mlsanity.com/paper/2402.13818/) (2024)
- [Jointly modelling the evolution of social structure and language in online communities](https://mlsanity.com/paper/2409.19243/) (2024)
- [Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech](https://mlsanity.com/paper/2502.09004/) (2025)
- [Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media](https://mlsanity.com/paper/2510.18582/) (2025)
- [Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames](https://mlsanity.com/paper/2604.20454/) (2026)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing](https://mlsanity.com/paper/2305.09548/) (2023)
- [A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media](https://mlsanity.com/paper/2404.07036/) (2024)
- [The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech](https://mlsanity.com/paper/2508.15524/) (2025)
- [Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language](https://mlsanity.com/paper/2204.10519/) (2022)
- [Down the Toxicity Rabbit Hole: A Novel Framework to Bias Audit Large Language Models](https://mlsanity.com/paper/2309.06415/) (2023)
- [A Survey on Stereotype Detection in Natural Language Processing](https://mlsanity.com/paper/2505.17642/) (2025)
- [Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance](https://mlsanity.com/paper/2607.13260/) (2026)
- [Discovering Conceptual Metaphors Across Topics and Media Types](https://mlsanity.com/paper/2608.06652/) (2026)
- [Counter with Evidence! A Multi-Agent Memory Efficient Reasoning Framework for Hate Category Informed Counterspeech Generation](https://mlsanity.com/paper/2608.23152/) (2026)

## Abstract (arXiv)

Dehumanization is a pernicious psychological process that often leads to extreme intergroup bias, hate speech, and violence aimed at targeted social groups. Despite these serious consequences and the wealth of available data, dehumanization has not yet been computationally studied on a large scale. Drawing upon social psychology research, we create a computational linguistic framework for analyzing dehumanizing language by identifying linguistic correlates of salient components of dehumanization. We then apply this framework to analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. Overall, we find increasingly humanizing descriptions of LGBTQ people over time. However, we find that the label homosexual has emerged to be much more strongly associated with dehumanizing attitudes than other labels, such as gay. Our proposed techniques highlight processes of linguistic variation and change in discourses surrounding marginalized groups. Furthermore, the ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.