ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian

Abstract:We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate speech detection in Persian. The dataset contains 31% hateful content and supports both hate detection and multi-label fine-grained target identification across seven structured target categories. ParsHate also distinguishes explicit and implicit hate, marks explicit and implicit targets, and provides span-level rationales. Data collection combines random and score-stratified temporal sampling to reduce keyword-driven bias while preserving natural label distributions. Applying SOTA models for Persian hate-speech detection on ParsHate shows moderate performance (79% F1), especially with samples from earlier years, and low performance with target identification (25.5% macro-F1). This emphasizes the diverse sampling of hate speech in ParsHate and its challenging nature that requires more advanced methods for better performance. Dataset is made publicly available.

Computation and Language (cs.CL); Databases (cs.DB)

arXiv:2609.16393 [cs.CL]

(or arXiv:2609.16393v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.16393 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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