From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech
This work addresses the problem of understanding and moderating fear speech, which is widespread and evades detection, for researchers and practitioners in computational linguistics and social media moderation, but it is incremental as it consolidates existing knowledge rather than introducing new methods.
The paper tackles the fragmented and under-resourced study of fear speech in computational linguistics by bridging cross-disciplinary theories and reviewing existing definitions and datasets, resulting in a proposed taxonomy to guide future research and dataset creation.
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.