Counter Hate Speech in Social Media: A Survey
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Abstract (arXiv)
With the high prevalence of offensive language against minorities in social media, counter-hate speeches (CHS) generation is considered an automatic way of tackling this challenge. The CHS is supposed to appear as a third voice to educate people and keep the social [red lines bold] without limiting the principles of freedom of speech. In this paper, we review the most important research in the past and present with a main focus on methodologies, collected datasets and statistical analysis CHS's impact on social media. The CHS generation is based on the optimistic assumption that any attempt to intervene the hate speech in social media can play a positive role in this context. Beyond that, previous works ignored the investigation of the sequence of comments before and after the CHS. However, the positive impact is not guaranteed, as shown in some previous works. To the best of our knowledge, no attempt has been made to survey the related work to compare the past research in terms of CHS's impact on social media. We take the first step in this direction by providing a comprehensive review on related works and categorizing them based on different factors including impact, methodology, data source, etc.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 65 references that are in the map. First posted on arXiv 21 Feb 2022.
Builds on
15 of 65 references.
References · 15
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
- 2016Deep Reinforcement Learning for Dialogue Generationcited by 286
- 2018CatBoost: gradient boosting with categorical features supportcited by 208
- 2014Recurrent Neural Network Regularizationcited by 189
- 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
- 2015A Random Forest Guided Tourcited by 168
- 2019A Benchmark Dataset for Learning to Intervene in Online Hate Speechcited by 128
- 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 109
- 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
- 2018Thou shalt not hate: Countering Online Hate Speechcited by 64
- 2020Generating Counter Narratives against Online Hate Speech: Data and Strategiescited by 58
3 more
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 2.
5 papers.
- 2024NLP for Counterspeech against Hate: A Survey and How-To Guidecited by 21
- 2023Understanding Counterspeech for Online Harm Mitigationcited by 17
- 2025HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigationcited by 1
- 2024Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Managementcited by 0
- 2025Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeechcited by 0
- 2025Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech6 Aug 2025
- 2025HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation6 Jul 2025
- 2024Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management27 Jun 2024
- 2024NLP for Counterspeech against Hate: A Survey and How-To Guide29 Mar 2024
- 2023Understanding Counterspeech for Online Harm Mitigation1 Jul 2023