Most toxicity detection models treat toxicity as an intrinsic property of text, overlooking the role of context in shaping its impact. In this position paper, drawing on insights from psychology, neuroscience, and computational social science, we reconceptualise toxicity as a socially emergent signal of stress. We formalise this perspective in the Contextual Stress Framework (CSF), which defines toxicity as a stress-inducing norm violation within a given context and introduces an additional dimension for toxicity detection. As one possible realisation of CSF, we introduce PONOS (Proportion Of Negative Observed Sentiments), a metric that quantifies toxicity through collective social reception rather than lexical features. We validate this approach on a novel dataset, demonstrating improved contextual sensitivity and adaptability when used alongside existing models.

翻译：大多数毒性检测模型将毒性视为文本的内在属性，忽视了语境在塑造其影响中的作用。在这篇立场论文中，我们借鉴心理学、神经科学和计算社会科学的见解，将毒性重新概念化为一种社会涌现的压力信号。我们在语境压力框架中形式化了这一观点，该框架将毒性定义为给定语境中引发压力的规范违反，并为毒性检测引入了一个额外的维度。作为CSF的一种可能实现，我们引入了PONOS，这是一种通过集体社会接收而非词汇特征来量化毒性的指标。我们在一个新数据集上验证了该方法，证明其与现有模型结合使用时，具有改进的语境敏感性和适应性。