mlsanityData to 14 Oct 2026

Assessing the Human Likeness of AI-Generated Counterspeech

arXiv 2410.11007 ↗First posted 14 Oct 2024cs.CLcited by 5

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Abstract (arXiv)

Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It effectively curbs the spread of hatred and fosters constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on relevance, surface form, and other shallow linguistic characteristics. This paper investigates the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity. The dataset used in this study is publicly available for further research.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

16 of 52 references.

References · 16

  1. 2019A Benchmark Dataset for Learning to Intervene in Online Hate Speechcited by 129
  2. 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 110
  3. 2021Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speechcited by 82
  4. 2021Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speechcited by 42
  5. 2021Towards Knowledge-Grounded Counter Narrative Generation for Hate Speechcited by 42
  6. 2022Hate Speech and Counter Speech Detection: Conversational Context Does Mattercited by 32
  7. 2022Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Studycited by 30
  8. 2022CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeechcited by 29
  9. 2023Understanding Counterspeech for Online Harm Mitigationcited by 17
  10. 2024On Zero-Shot Counterspeech Generation by LLMscited by 17
  11. 2024Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicatedcited by 15
  12. 2023DisCGen: A Framework for Discourse-Informed Counterspeech Generationcited by 14
4 more
  1. 2023Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generationcited by 13
  2. 2024Outcome-Constrained Large Language Models for Countering Hate Speechcited by 12
  3. 2023Hate Cannot Drive out Hate: Forecasting Conversation Incivility following Replies to Hate Speechcited by 10
  4. 2024Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughtscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformationcited by 3
  2. 2025Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Studycited by 2
  3. 2026Multilingual Hate Speech Detection and Counterspeech Generation: A Comprehensive Survey and Practical Guidecited by 1
  4. 2026Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeechcited by 0
  5. 2026Counter with Evidence! A Multi-Agent Memory Efficient Reasoning Framework for Hate Category Informed Counterspeech Generationcited by 0