# From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech

arXiv: https://arxiv.org/abs/2601.17132 · HTML: https://mlsanity.com/paper/2601.17132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Open Source Software for Efficient and Transparent Reviews](https://mlsanity.com/paper/2006.12166/) (2020)
- [Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection](https://mlsanity.com/paper/1910.09982/) (2019)
- [Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles](https://mlsanity.com/paper/2402.17478/) (2024)
- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs](https://mlsanity.com/paper/2412.15993/) (2024)
- [An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text](https://mlsanity.com/paper/1912.06745/) (2019)
- [Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions](https://mlsanity.com/paper/2101.11956/) (2021)
- [Multi-Modal Framing Analysis of News](https://mlsanity.com/paper/2503.20960/) (2025)
- [Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.06710/) (2022)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [Generalizability of Media Frames: Corpus creation and analysis across countries](https://mlsanity.com/paper/2506.16337/) (2025)
- [Experiments in Detecting Persuasion Techniques in the News](https://mlsanity.com/paper/1911.06815/) (2019)
- [Towards a Computational Analysis of Suspense: Detecting Dangerous Situations](https://mlsanity.com/paper/2305.06818/) (2023)
- [Multi-Platform Framing Analysis: A Case Study of Kristiansand Quran Burning](https://mlsanity.com/paper/2407.12968/) (2024)
- [Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMs](https://mlsanity.com/paper/2506.00061/) (2025)
- [Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging](https://mlsanity.com/paper/2507.17409/) (2025)
- [Analyzing Biases in Political Dialogue: Tagging U.S. Presidential Debates with an Extended DAMSL Framework](https://mlsanity.com/paper/2505.19515/) (2025)
- [Beyond the Battlefield: Framing Analysis of Media Coverage in Conflict Reporting](https://mlsanity.com/paper/2506.10421/) (2025)

## Abstract (arXiv)

Few forces rival fear in their ability to mobilize societies, distort communication, and reshape collective behavior. In computational linguistics, fear is primarily studied as an emotion, but not as a distinct form of speech. Fear speech content is widespread and growing, and often outperforms hate-speech content in reach and engagement because it appears "civiler" and evades moderation. Yet the computational study of fear speech remains fragmented and under-resourced. This can be understood by recognizing that fear speech is a phenomenon shaped by contributions from multiple disciplines. In this paper, we bridge cross-disciplinary perspectives by comparing theories of fear from Psychology, Political science, Communication science, and Linguistics. Building on this, we review existing definitions. We follow up with a survey of datasets from related research areas and propose a taxonomy that consolidates different dimensions of fear for studying fear speech. By reviewing current datasets and defining core concepts, our work offers both theoretical and practical guidance for creating datasets and advancing fear speech research.