Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Counterspeech, i.e. responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship risks of deletion-based content moderation. |
| Approach: | They draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. |
| Outcome: | The strategies used in human- and machine-generated counterspeech datasets are convincing, whereas human-written counterspech uses less specific strategies compared to machine-produced counters. |
Similar Papers
Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics. |
| Approach: | They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies . |
| Outcome: | The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans. |
NLP for Counterspeech against Hate: A Survey and How-To Guide (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have focused on the challenges of analysing, collecting, classifying, and automatically generating counterspeech, to reduce the huge burden of manually producing it. |
| Approach: | They propose a guide for doing research on counterspeech, with detailed examples and best practices that can be learnt from the NLP community. |
| Outcome: | The proposed strategies can reduce online and offline violence while preserving the freedom of speech of the users. |
Outcome-Constrained Large Language Models for Countering Hate Speech (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informative, and intent-driven. |
| Approach: | They develop automatic counterspeech generation methods that incorporate two desired conversation outcomes into the text generation process: low conversation incivility and non-hateful hater reentry. |
| Outcome: | The proposed methods incorporate two desired conversation outcomes: low conversation incivility and non-hateful hater reentry. |
Echoes of Discord: Forecasting Hater Reactions to Counterspeech (2025.findings-naacl)
Copied to clipboard
| Challenge: | Hate speech (HS) online causes increased prejudice and discrimination, fostering an environment of hostility and social division. |
| Approach: | They analyze the Reddit Echoes of Hate dataset to assess the impact of counterspeech from the hater's perspective and focus on whether the counterspeak leads the reentry to be hateful. |
| Outcome: | The proposed model outperforms the two-stage reaction predictor and the three-way classifier to predict haters' reactions to the reentry of the conversation and determines the type of resentment. |
A Fine-Grained Taxonomy of Replies to Hate Speech (2023.emnlp-main)
Copied to clipboard
| Challenge: | a new corpus of responses to hate speech is developed to counter hate speech . authors work with real, user-generated hate speech and all the replies it elicits . counterspeech refers to a "direct response that counters hate speech" |
| Approach: | They propose a taxonomy of responses to hate speech and a new corpus to analyze responses . they find that responses to user-generated hate speech are more effective than replies generated by a third party . |
| Outcome: | The proposed taxonomy of responses to hate speech and a new corpus provide insights into content real users reply with and which replies are empirically most effective. |
Counterspeech Generation using Small Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Social media use is growing annually with about 68.5% of the global population active on these platforms as of July 2025. |
| Approach: | They evaluate SLMs ranging from 100 million to 3 billion parameters using simple prompting strategies as well as fine-tuning, combining automatic and robust human evaluations. |
| Outcome: | The proposed models generate relevant, coherent, and high-quality counterspeech, suggesting their suitability for efficient and responsible deployments. |
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
Copied to clipboard
| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
| Approach: | They propose a three-module pipeline approach to generate diverse and relevant counterspeech . they first generate various counterspeak candidates by a generative model, then filter ungrammatical ones using a BERT model . |
| Outcome: | The proposed pipeline generates diverse and relevant counterspeech responses on three datasets. |
NLP for Counterspeech against Hate and Misinformation (CSHAM) (2025.acl-tutorials)
Copied to clipboard
| Challenge: | tutorial aims to show how counterspeech is used to tackle abuse and misinformation by individuals, activists and organisations. |
| Approach: | tutorial aims to show how counterspeech is currently used to tackle abuse and misinformation . will also show how Natural Language Processing (NLP) and Generation (NLG) can be applied to automate its production. |
| Outcome: | The tutorial will bring diverse multidisciplinary perspectives to safety research . case studies from industry and public policy will be included . |
Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Automated responses lack argumentative richness which characterises expert-produced counterspeech. |
| Approach: | They propose to automate counterspeech generation by investigating tension between helpfulness and harmlessness of LLMs and to assess whether presence of safety guardrails hinders quality of generations. |
| Outcome: | The proposed approach produces more cogent responses that lack argumentative richness which characterises expert-produced counterspeech. |
Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing systems that target hate speech with intent-conditioned counterspeech generate better results with longer contexts. |
| Approach: | They propose a framework that enables counterspeech generation by modeling the pragmatic implications underlying social biases in hateful statements. |
| Outcome: | The proposed framework outperforms existing benchmarks in intent-conditioned counterspeech generation. |