Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (2024.emnlp-main)
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| Challenge: | scalable strategies to combat online misinformation are short-term and insufficient, authors say . current reactive approaches, like content flagging and banning, do little to change perception of misinformants . human evaluations show that our framework generates expert-like responses . |
| Approach: | They propose a framework that generates persuasive responses from hate-speech counter-responses . human evaluations show that the framework generates expert-like responses . |
| Outcome: | The proposed framework generates expert-like responses and is 14% more engaging, 21% more natural, and 18% more factual than the best available alternatives. |
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Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
LLM generated responses to mitigate the impact of hate speech (2024.findings-emnlp)
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Jakub Podolak, Szymon Łukasik, Paweł Balawender, Jan Ossowski, Jan Piotrowski, Katarzyna Bakowicz, Piotr Sankowski
| Challenge: | a study aims to determine the effectiveness of large language models to counteract hate speech . it is the first real-life A/B test evaluating the effectiveness . |
| Approach: | They conduct the first real-life A/B test assessing the effectiveness of LLM-generated counter-speech. |
| Outcome: | The proposed system reduces user engagement by over 20%, the study shows . the proposed metric is based on a simple metric and is scalable to other platforms . |
Countering Hateful and Offensive Speech Online - Open Challenges (2024.emnlp-tutorials)
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Leon Derczynski, Marco Guerini, Debora Nozza, Flor Miriam Plaza-del-Arco, Jeffrey Sorensen, Marcos Zampieri
| Challenge: | a comprehensive understanding of the field is needed to maintain respectful and inclusive online environments. |
| Approach: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
| Outcome: | This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches. |
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)
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Jason Liartis, Eirini Kaldeli, Lamprini Gyftokosta, Eleftherios Chelioudakis, Orfeas Menis Mastromichalakis
| Challenge: | a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say . |
| Approach: | They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek. |
| Outcome: | The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear . |
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study (2022.findings-acl)
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| Challenge: | Autoregressive models combined with stochastic decodings are the most promising for generating CNs with regard to an unseen target of hate. |
| Approach: | They propose to use pre-trained language models to generate counter-narratives in English by adding an automatic post-editing step to refine generated CNs. |
| Outcome: | The proposed pipeline could be used to generate counter-narratives in English using pre-trained language models and stochastic decoding mechanisms. |
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning (2023.findings-emnlp)
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| Challenge: | Recent benchmarks have attempted to identify and explain hate speech but lack the reasoning to supervise detection models. |
| Approach: | They propose a framework that uses large language models to fill in the gaps in hate speech explanations by using existing annotations. |
| Outcome: | The proposed framework outperforms baselines on SBIC and Implicit Hate using model-generated data and improves generalization to unseen datasets. |
Decoding Hate: Exploring Language Models’ Reactions to Hate Speech (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) are trained on vast amounts of unmoderated internet data, enabling them to generate text autonomously. |
| Approach: | They investigate the responses of seven state-of-the-art Large Language Models (LLMs) to hate speech by qualitative analysis. |
| Outcome: | The proposed models can handle hate speech inputs and mitigate it through fine-tuning and guideline guardrailing. |
Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models (2024.findings-emnlp)
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| Challenge: | HS is any communication demeaning a person or a group based on social or ethnic characteristics that undermines social harmony and individual safety . the recent Israel-Hamas conflict has escalated both anti-Muslim and anti-Semitic sentiments worldwide . |
| Approach: | They examine the role of large language models and large multimodal models in HS moderation . they examine how text, images, and audio interact to spread hate speech . |
| Outcome: | The findings highlight the need for solutions in low-resource settings and highlight the gaps in existing methods. |
Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech (2024.findings-emnlp)
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| Challenge: | Hate speech (HS) is a widespread problem in society with severe repercussions at both personal and societal levels. |
| Approach: | They propose to incorporate conversational history into CNs to confront biases and stereotypes driving hateful narratives. |
| Outcome: | The proposed strategies outperform existing methods on comparing graphical and text representations with varying degrees of context. |
Improving Hate Speech Detection by Fusing Textual and User Interaction Representations in Online Communities (2026.acl-industry)
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| Challenge: | Existing studies on toxic content in online communities are limited by the scarcity of data that align textual content with comprehensive social interactions. |
| Approach: | They propose a user-aware hate speech detection framework that effectively fuses textual semantics with social interaction representations to provide pragmatic context for disambiguation. |
| Outcome: | The proposed framework outperforms strong text-only baselines by over 3.6%, validating the critical role of social context in enhancing detection accuracy. |