Researchers propose FAID, a fine-grained adaptive framework to detect implicit hate speech more efficiently

Researchers have introduced a novel framework called Fine-grained Adaptive Implicit Hate speech Detection (FAID) to improve the detection of implicit hate speech, which often conceals malice within seemingly compliant expressions such as metaphors or contextual hints rather than explicit profanity.

The FAID framework begins by classifying implicit hate speech into three fine-grained categories: Shallow, Targeted, and Context-Dependent. This categorization reflects the observation that online hate speech is not monolithic but varies in linguistic nuance and complexity.

For Shallow samples, where malicious intent is surface-identifiable, FAID employs lightweight prompt-tuning to enable rapid classification with minimal computational overhead. Targeted samples, which bind malicious intent to concealed targets, are addressed using a knowledge augmentation technique that iteratively refines the model to reveal hidden targets. Context-Dependent samples, which lack sufficient background information, are handled by an agentic framework that automatically generates prompts to evolve context, infer missing information, and identify ambiguous malicious intents.

The researchers evaluated FAID on four benchmark datasets and report that it significantly outperforms state-of-the-art baselines, demonstrating both improved accuracy and efficiency in detecting implicit hate speech across diverse linguistic contexts.