Toxicity Is a Property of Communicative Events: A Contextual Stress Measurement Framework for NLP

Abstract:Toxicity detection has become core safety infrastructure for online moderation, dataset filtering, and deployed language-model systems. Yet most detectors still treat toxicity as an intrinsic property of isolated text. This position paper argues that toxicity detection should be evaluated as the contextual measurement of situated communicative harm, rather than as single-label text classification. Toxicity is not contained in words alone; it emerges when a communicative act is interpreted by an audience within a normative and social context. We introduce the Contextual Stress Framework (CSF), which defines toxicity as a relation between perceived norm violation and induced stress or disruption. CSF explains why text-intrinsic detectors overflag dialectal or reclaimed language, miss coded or pragmatic abuse, and remain brittle under meaning-preserving transformations.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2503.16072 [cs.LG]

(or arXiv:2503.16072v5 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2503.16072 Focus to learn more arXiv-issued DOI via DataCite

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