# Hate Cannot Drive out Hate: Forecasting Conversation Incivility following Replies to Hate Speech

arXiv: https://arxiv.org/abs/2312.04804 · HTML: https://mlsanity.com/paper/2312.04804/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 74 references that are in the map.
- First posted on arXiv 8 Dec 2023.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (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)
- [Thou shalt not hate: Countering Online Hate Speech](https://mlsanity.com/paper/1808.04409/) (2018)
- [Generating Counter Narratives against Online Hate Speech: Data and Strategies](https://mlsanity.com/paper/2004.04216/) (2020)
- [jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models](https://mlsanity.com/paper/2003.02249/) (2020)
- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations](https://mlsanity.com/paper/2102.08368/) (2021)
- [Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game](https://mlsanity.com/paper/1506.04744/) (2015)
- [Detecting Attackable Sentences in Arguments](https://mlsanity.com/paper/2010.02660/) (2020)
- [What Makes Online Communities 'Better'? Measuring Values, Consensus, and Conflict across Thousands of Subreddits](https://mlsanity.com/paper/2111.05835/) (2021)
- [Forecasting the presence and intensity of hostility on Instagram using linguistic and social features](https://mlsanity.com/paper/1804.06759/) (2018)
- [Making Online Communities 'Better': A Taxonomy of Community Values on Reddit](https://mlsanity.com/paper/2109.05152/) (2021)
- [A Study of Cyber Hate on Twitter with Implications for Social Media Governance Strategies](https://mlsanity.com/paper/1908.11732/) (2019)

## Built on by (most cited first, 10 of 10)

- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- [Outcome-Constrained Large Language Models for Countering Hate Speech](https://mlsanity.com/paper/2403.17146/) (2024)
- [Decoding Hate: Exploring Language Models' Reactions to Hate Speech](https://mlsanity.com/paper/2410.00775/) (2024)
- [Assessing the Human Likeness of AI-Generated Counterspeech](https://mlsanity.com/paper/2410.11007/) (2024)
- [Echoes of Discord: Forecasting Hater Reactions to Counterspeech](https://mlsanity.com/paper/2501.16235/) (2025)
- [Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study](https://mlsanity.com/paper/2506.11919/) (2025)
- [Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI](https://mlsanity.com/paper/2407.20274/) (2024)
- ["Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth](https://mlsanity.com/paper/2508.02094/) (2025)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)
- [Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech](https://mlsanity.com/paper/2607.26236/) (2026)

## Abstract (arXiv)

User-generated replies to hate speech are promising means to combat hatred, but questions about whether they can stop incivility in follow-up conversations linger. We argue that effective replies stop incivility from emerging in follow-up conversations - replies that elicit more incivility are counterproductive. This study introduces the task of predicting the incivility of conversations following replies to hate speech. We first propose a metric to measure conversation incivility based on the number of civil and uncivil comments as well as the unique authors involved in the discourse. Our metric approximates human judgments more accurately than previous metrics. We then use the metric to evaluate the outcomes of replies to hate speech. A linguistic analysis uncovers the differences in the language of replies that elicit follow-up conversations with high and low incivility. Experimental results show that forecasting incivility is challenging. We close with a qualitative analysis shedding light into the most common errors made by the best model.