From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech

Key points

• Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation.1
• Persian, a relatively low-resource language, lacks sufficient annotated data and computational resources for fine-grained hate speech analysis.1
• By evaluating state-of-the-art large language models (LLMs), we find that performance generally declines as tasks require more fine-grained semantic and inferential reasoning.1
• Overall, this work provides insights into LLM-based hate speech analysis and supports more accountable moderation of Persian-language social media.1

Sources (1)

• [1]From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:42 PM

  In this study, we introduce a fine-grained, multi-hop benchmark for explainable Persian hate speech analysis, covering content triage, target identification, hate tactic and moral foundation analysis, textual evidence extraction, harmful narrative inference, and severity assessment.

  Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation.

Extractive summary: sentences quoted from the sources.