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

2026-01-23

AI Summary

Fear speech has long been underexplored in computational linguistics, often reduced to an emotion rather than recognized as a distinct discursive phenomenon, resulting in conceptual ambiguity, scarce resources, and insufficient interdisciplinary integration. This work addresses this gap by synthesizing theoretical perspectives from psychology, political science, communication studies, and linguistics. Through conceptual analysis, a review of existing datasets, and an interdisciplinary literature synthesis, it proposes the first integrative theoretical framework and multidimensional taxonomy for fear speech. By clearly establishing fear speech as an independent discursive form, this study not only delineates a coherent research trajectory but also provides a foundational theoretical basis and practical guidance for future dataset construction and computational modeling, thereby filling a critical void in the field.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• fear speech
• computational linguistics
• emotion
• content moderation
• interdisciplinary research

Innovation

• fear speech
• cross-disciplinary integration
• taxonomy
• computational linguistics
• dataset curation