ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian
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Computer Science > Computation and Language
arXiv:2609.16393 (cs)
[Submitted on 14 Sep 2026]
Title:ParsHate: A Benchmark Dataset for Hate and Target Detection in Persian
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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.
| Comments: | Accepted to EMNLP 2026 (Main Conference) |
| Subjects: | Computation and Language (cs.CL); Databases (cs.DB) |
| Cite as: | arXiv:2609.16393 [cs.CL] |
| (or arXiv:2609.16393v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16393
arXiv-issued DOI via DataCite (pending registration)
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