Redefining Toxicity: An Objective and Context-Aware Approach for Stress-Level-Based Detection
This work addresses the challenge of more accurate and context-aware toxicity detection for online platforms and content moderation systems, representing an incremental improvement over existing methods.
The paper tackled the problem of toxicity detection by reconceptualizing toxicity as a socially emergent stress signal rather than an intrinsic property of text, and introduced a new framework that demonstrated improved contextual sensitivity and adaptability on a novel dataset.
Most toxicity detection models treat toxicity as an intrinsic property of text, overlooking the role of context in shaping its impact. Drawing on interdisciplinary research, we reconceptualise toxicity as a socially emergent stress signal. We introduce a new framework for toxicity detection, including a formal definition and metric, and validate our approach on a novel dataset, demonstrating improved contextual sensitivity and adaptability.