# Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech

arXiv: https://arxiv.org/abs/2403.15449 · HTML: https://mlsanity.com/paper/2403.15449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 68 references that are in the map.
- First posted on arXiv 18 Mar 2024.

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

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [Investigating Data Contamination for Pre-training Language Models](https://mlsanity.com/paper/2401.06059/) (2024)
- [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)
- [Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media](https://mlsanity.com/paper/2104.08116/) (2021)
- [On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research](https://mlsanity.com/paper/2304.12397/) (2023)
- [Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering](https://mlsanity.com/paper/2211.03433/) (2022)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection](https://mlsanity.com/paper/2311.01270/) (2023)
- [Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language](https://mlsanity.com/paper/2311.00161/) (2023)
- [Countering hate on social media: Large scale classification of hate and counter speech](https://mlsanity.com/paper/2006.01974/) (2020)
- [Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement](https://mlsanity.com/paper/2310.18440/) (2023)
- [Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests](https://mlsanity.com/paper/2101.06351/) (2021)
- [Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation](https://mlsanity.com/paper/2305.13776/) (2023)
- [Changing Views: Persuasion Modeling and Argument Extraction from Online Discussions](https://mlsanity.com/paper/1907.06076/) (2019)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy](https://mlsanity.com/paper/2205.07233/) (2022)
- [Riveter: Measuring Power and Social Dynamics Between Entities](https://mlsanity.com/paper/2312.09536/) (2023)

## Built on by (most cited first, 2 of 2)

- [Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech](https://mlsanity.com/paper/2508.04638/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

Examining the factors that the counterspeech uses are at the core of understanding the optimal methods for confronting hate speech online. Various studies have assessed the emotional base factors used in counter speech, such as emotional empathy, offensiveness, and hostility. To better understand the counterspeech used in conversations, this study distills persuasion modes into reason, emotion, and credibility and evaluates their use in two types of conversation interactions: closed (multi-turn) and open (single-turn) concerning racism, sexism, and religious bigotry. The evaluation covers the distinct behaviors seen with human-sourced as opposed to machine-generated counterspeech. It also assesses the interplay between the stance taken and the mode of persuasion seen in the counterspeech. Notably, we observe nuanced differences in the counterspeech persuasion modes used in open and closed interactions, especially in terms of the topic, with a general tendency to use reason as a persuasion mode to express the counterpoint to hate comments. The machine-generated counterspeech tends to exhibit an emotional persuasion mode, while human counters lean toward reason. Furthermore, our study shows that reason tends to obtain more supportive replies than other persuasion modes. The findings highlight the potential for incorporating persuasion modes into studies about countering hate speech, as they can serve as an optimal means of explainability and pave the way for the further adoption of the reply's stance and the role it plays in assessing what comprises the optimal counterspeech.