Aligning Implied Statements for Implicit Hate Speech Generalizability with Context-Bounded Semi-hard Negative Mining
arXiv:2606.18852v1 Announce Type: cross Abstract: Classifying implicit hate speech remains a challenge, as intent is often masked through insinuation and context rather than explicit slurs. Prior supervised contrastive approaches improve in-domain detection but can overfit surface cues and struggle to transfer across datasets. We propose ImpSH, a triplet-based framework that aligns posts with implied statements when available and uses context-bounded semi-hard negatives to focus learning on near confusions. We also examine AugSH, which forms positives via data augmentation. In controlled evalu
The proliferation of AI-generated content and increasingly sophisticated online discourse necessitates more robust methods for detecting nuanced harmful speech.
Improving the detection of implicit hate speech is crucial for mitigating online toxicity, protecting vulnerable populations, and maintaining platform integrity.
New computational methods like ImpSH could significantly enhance the accuracy and generalizability of identifying veiled harmful content, moving beyond explicit keywords.
- · Social media platforms
- · AI safety researchers
- · Online moderators
- · Perpetrators of implicit hate speech
- · Bots and accounts spreading nuanced misinformation
More effective identification and removal of subtle harmful content from online platforms.
Increased pressure on users to communicate within platform guidelines, potentially leading to 'chilling effects' or new forms of evasion.
The development of more advanced adversarial AI techniques to circumvent detection, creating an ongoing technological arms race.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI