# Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate

arXiv: https://arxiv.org/abs/2403.00179 · HTML: https://mlsanity.com/paper/2403.00179/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

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

- [Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations](https://mlsanity.com/paper/2503.01623/) (2025)
- [Outcome-Constrained Large Language Models for Countering Hate Speech](https://mlsanity.com/paper/2403.17146/) (2024)
- [Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse](https://mlsanity.com/paper/2410.00175/) (2024)
- [Attention to Non-Adopters](https://mlsanity.com/paper/2510.15951/) (2025)
- [Debunking with Dialogue? Exploring AI-Generated Counterspeech to Challenge Conspiracy Theories](https://mlsanity.com/paper/2504.16604/) (2025)
- [When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails](https://mlsanity.com/paper/2407.06323/) (2024)
- [HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation](https://mlsanity.com/paper/2507.04350/) (2025)
- [Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks](https://mlsanity.com/paper/2603.21404/) (2026)
- [Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation](https://mlsanity.com/paper/2605.22435/) (2026)
- [Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management](https://mlsanity.com/paper/2406.19543/) (2024)
- [Designing Human-AI Collaboration to Support Learning in Counterspeech Writing](https://mlsanity.com/paper/2410.03032/) (2024)
- [Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech](https://mlsanity.com/paper/2508.04638/) (2025)
- [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)
- [How Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities](https://mlsanity.com/paper/2610.04803/) (2026)

## Abstract (arXiv)

Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.