# Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation

arXiv: https://arxiv.org/abs/2412.07338 · HTML: https://mlsanity.com/paper/2412.07338/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 75 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 30 of 35)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [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)
- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Generating Counter Narratives against Online Hate Speech: Data and Strategies](https://mlsanity.com/paper/2004.04216/) (2020)
- [Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech](https://mlsanity.com/paper/2106.01625/) (2021)
- [Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech](https://mlsanity.com/paper/2106.11783/) (2021)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study](https://mlsanity.com/paper/2204.01440/) (2022)
- [Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies](https://mlsanity.com/paper/2001.04564/) (2020)
- [CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech](https://mlsanity.com/paper/2205.04304/) (2022)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [Understanding Counterspeech for Online Harm Mitigation](https://mlsanity.com/paper/2307.04761/) (2023)
- [Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit](https://mlsanity.com/paper/2009.11483/) (2020)
- [Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels](https://mlsanity.com/paper/2010.10397/) (2020)
- [DisCGen: A Framework for Discourse-Informed Counterspeech Generation](https://mlsanity.com/paper/2311.18147/) (2023)
- [Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2201.06455/) (2022)
- [Can Language Model Moderators Improve the Health of Online Discourse?](https://mlsanity.com/paper/2311.10781/) (2023)
- [Impact and dynamics of hate and counter speech online](https://mlsanity.com/paper/2009.08392/) (2020)
- [Hate Cannot Drive out Hate: Forecasting Conversation Incivility following Replies to Hate Speech](https://mlsanity.com/paper/2312.04804/) (2023)
- [The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media](https://mlsanity.com/paper/2312.10269/) (2023)
- [A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation](https://mlsanity.com/paper/2406.15227/) (2024)
- [One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2209.08809/) (2022)
- [The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit](https://mlsanity.com/paper/2401.11254/) (2024)

## Built on by (most cited first, 15 of 15)

- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [Hallucination Detection: A Probabilistic Framework Using Embeddings Distance Analysis](https://mlsanity.com/paper/2502.08663/) (2025)
- [Debunking with Dialogue? Exploring AI-Generated Counterspeech to Challenge Conspiracy Theories](https://mlsanity.com/paper/2504.16604/) (2025)
- [Algorithmic Fairness in NLP: Persona-Infused LLMs for Human-Centric Hate Speech Detection](https://mlsanity.com/paper/2510.19331/) (2025)
- [MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications](https://mlsanity.com/paper/2601.06193/) (2026)
- [Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation](https://mlsanity.com/paper/2605.22435/) (2026)
- [Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech](https://mlsanity.com/paper/2508.04638/) (2025)
- [PRISM: Phase-enhanced Radial-based Image Signature Mapping framework for fingerprinting AI-generated images](https://mlsanity.com/paper/2509.15270/) (2025)
- [Quantifying Feature Importance for Online Content Moderation](https://mlsanity.com/paper/2510.19882/) (2025)
- [Evaluation of Multilingual LLMs Personalized Text Generation Capabilities Targeting Groups and Social-Media Platforms](https://mlsanity.com/paper/2601.03752/) (2026)
- [Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation](https://mlsanity.com/paper/2604.17020/) (2026)
- [Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech](https://mlsanity.com/paper/2607.26236/) (2026)
- [Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention](https://mlsanity.com/paper/2608.21721/) (2026)
- [Counter with Evidence! A Multi-Agent Memory Efficient Reasoning Framework for Hate Category Informed Counterspeech Generation](https://mlsanity.com/paper/2608.23152/) (2026)

## Abstract (arXiv)

AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.