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ParsCN: A Persian Dataset for Counter-Narrative Generation to Combat Online Hate Speech

Zahra Safdari Fesaghandis, Suman Kalyan Maity

Abstract

Online hate speech threatens online civility, particularly in low-resource and multilingual environments. Counter-narratives offer a promising solution by promoting constructive responses to hate speech. However, automatic counter-narrative generation is hindered by the lack of high-quality data for low-resource languages like Persian. To bridge this gap, we introduce ParsCN, the first and most comprehensive Persian counter-narrative dataset. Consisting of 1,100 hate speech and counter-narrative pairs, it provides fine-grained annotations across six target groups and six countering strategies, tailored to the socio-cultural context of Persian online discourse. We propose a novel, scalable multi-stage framework that integrates culturally-informed human annotation with few-shot LLM-augmented generation, guided by semantic retrieval and rigorous manual curation. This approach enables the creation of diverse, high-quality counter-narratives while significantly reducing annotation costs - establishing a replicable paradigm for other low-resource settings. Comprehensive human and automatic evaluations confirm the quality of the dataset and the effectiveness of the generated responses. Human-written counter-narratives achieved the highest scores for relevance (4.23), Effectiveness (4.21), fluency (4.92), and tone appropriateness (4.79), with GPT-4o and Claude closely following. Automatic evaluations show strong semantic alignment, high lexical diversity, and low toxicity across all sources. Finally, we conduct benchmark evaluations using mBART and PersianMind on a held-out test set. Results reveal that existing models struggle with fluency, cultural nuance, and safety - highlighting the need for Persian-specific resources like ParsCN. Our dataset serves as a foundational benchmark to advance research on Persian counter-narrative generation and foster safer, more inclusive digital spaces.

ParsCN: A Persian Dataset for Counter-Narrative Generation to Combat Online Hate Speech

Abstract

Online hate speech threatens online civility, particularly in low-resource and multilingual environments. Counter-narratives offer a promising solution by promoting constructive responses to hate speech. However, automatic counter-narrative generation is hindered by the lack of high-quality data for low-resource languages like Persian. To bridge this gap, we introduce ParsCN, the first and most comprehensive Persian counter-narrative dataset. Consisting of 1,100 hate speech and counter-narrative pairs, it provides fine-grained annotations across six target groups and six countering strategies, tailored to the socio-cultural context of Persian online discourse. We propose a novel, scalable multi-stage framework that integrates culturally-informed human annotation with few-shot LLM-augmented generation, guided by semantic retrieval and rigorous manual curation. This approach enables the creation of diverse, high-quality counter-narratives while significantly reducing annotation costs - establishing a replicable paradigm for other low-resource settings. Comprehensive human and automatic evaluations confirm the quality of the dataset and the effectiveness of the generated responses. Human-written counter-narratives achieved the highest scores for relevance (4.23), Effectiveness (4.21), fluency (4.92), and tone appropriateness (4.79), with GPT-4o and Claude closely following. Automatic evaluations show strong semantic alignment, high lexical diversity, and low toxicity across all sources. Finally, we conduct benchmark evaluations using mBART and PersianMind on a held-out test set. Results reveal that existing models struggle with fluency, cultural nuance, and safety - highlighting the need for Persian-specific resources like ParsCN. Our dataset serves as a foundational benchmark to advance research on Persian counter-narrative generation and foster safer, more inclusive digital spaces.

Paper Structure

This paper contains 43 sections, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Counter-Narrative Dataset Creation
  4. Counter-Narrative Generation Methodologies
  5. Dataset
  6. Hate Speech Datasets
  7. Hate Speech Categories
  8. Counter-Narrative Approaches
  9. Stage One: Data Collection and Annotation
  10. Data Sourcing
  11. Counter-Narrative Annotation
  12. MT-CONAN Integration
  13. Stage Two: Automated Counter-Narrative Generation
  14. Expansion of Dataset Sourcing
  15. Retrieval of Relevant Examples
  16. ...and 28 more sections

Figures (3)

  • Figure 1: ParsCN Dataset Creation Pipeline
  • Figure 2: Examples of Hate Speech and Counter Narratives, Along with Their Target Groups and Counter Types, from ParsCN
  • Figure 3: Examples of Culturally Relevant Hate Speech and Counter-Narratives from MT-CONAN and HateXplain with English Translations