# Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language

arXiv: https://arxiv.org/abs/2311.00161 · HTML: https://mlsanity.com/paper/2311.00161/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech](https://mlsanity.com/paper/2107.08720/) (2021)
- [Thou shalt not hate: Countering Online Hate Speech](https://mlsanity.com/paper/1808.04409/) (2018)
- [Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech](https://mlsanity.com/paper/2106.01625/) (2021)
- [Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering](https://mlsanity.com/paper/2211.03433/) (2022)
- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech](https://mlsanity.com/paper/2205.04304/) (2022)
- [Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model](https://mlsanity.com/paper/2106.02596/) (2021)
- [Countering hate on social media: Large scale classification of hate and counter speech](https://mlsanity.com/paper/2006.01974/) (2020)
- [What Makes Online Communities 'Better'? Measuring Values, Consensus, and Conflict across Thousands of Subreddits](https://mlsanity.com/paper/2111.05835/) (2021)
- [Collective moderation of hate, toxicity, and extremity in online discussions](https://mlsanity.com/paper/2303.00357/) (2023)
- [Towards Countering Essentialism through Social Bias Reasoning](https://mlsanity.com/paper/2303.16173/) (2023)
- [Discussion quality diffuses in the digital public square](https://mlsanity.com/paper/1702.06677/) (2017)

## Built on by (most cited first, 24 of 24)

- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps](https://mlsanity.com/paper/2404.01651/) (2024)
- [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://mlsanity.com/paper/2404.02037/) (2024)
- [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024)
- [Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering](https://mlsanity.com/paper/2410.03466/) (2024)
- [How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies](https://mlsanity.com/paper/2407.11733/) (2024)
- [Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization](https://mlsanity.com/paper/2412.15453/) (2024)
- [CSEval: Towards Automated, Multi-Dimensional, and Reference-Free Counterspeech Evaluation using Auto-Calibrated LLMs](https://mlsanity.com/paper/2501.17581/) (2025)
- [Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling](https://mlsanity.com/paper/2410.04164/) (2024)
- [Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech](https://mlsanity.com/paper/2403.15449/) (2024)
- [Debunking with Dialogue? Exploring AI-Generated Counterspeech to Challenge Conspiracy Theories](https://mlsanity.com/paper/2504.16604/) (2025)
- [Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study](https://mlsanity.com/paper/2506.11919/) (2025)
- [When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails](https://mlsanity.com/paper/2407.06323/) (2024)
- [How Do Users Negotiate Harmful Value Conflicts with AI Companions? A Study with Minion, a Technology Probe for In-Situ Human-AI Conflict Response](https://mlsanity.com/paper/2411.07042/) (2024)
- [Generics are puzzling. Can language models find the missing piece?](https://mlsanity.com/paper/2412.11318/) (2024)
- [Theories of "Sexuality" in Natural Language Processing Bias Research](https://mlsanity.com/paper/2506.22481/) (2025)
- [Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation](https://mlsanity.com/paper/2605.22435/) (2026)
- [Designing Human-AI Collaboration to Support Learning in Counterspeech Writing](https://mlsanity.com/paper/2410.03032/) (2024)
- [CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges](https://mlsanity.com/paper/2606.20369/) (2026)
- [Counter with Evidence! A Multi-Agent Memory Efficient Reasoning Framework for Hate Category Informed Counterspeech Generation](https://mlsanity.com/paper/2608.23152/) (2026)
- [Deconstructing Stereotypes: Scope-Conditioned Generation for Effective Multilingual Counterspeech](https://mlsanity.com/paper/2609.16906/) (2026)
- [How Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities](https://mlsanity.com/paper/2610.04803/) (2026)
- [GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria](https://mlsanity.com/paper/2610.17778/) (2026)

## Abstract (arXiv)

Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype's origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine-generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.