Papers with detecting hate speech
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. |
Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster (2024.acl-short)
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Agostina Calabrese, Leonardo Neves, Neil Shah, Maarten Bos, Björn Ross, Mirella Lapata, Francesco Barbieri
| Challenge: | Existing studies have shown that explanations can support content moderators to make faster decisions, but the benefits of such models have not been studied. |
| Approach: | They propose to use structured explanations to support content moderators to make faster decisions by 7.4%. |
| Outcome: | The proposed models lower the speed of real-world moderators by 7.4% compared to generic explanations and are often ignored . previous studies have shown that explanations can support moderator's decision making by detecting violations of policies but the benefits have not been studied . |
CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network (2023.emnlp-main)
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| Challenge: | Existing work on detecting explicit hate speech has focused on indirect or coded language. |
| Approach: | They propose a context synergized neural network that integrates user- and conversational-contexts for detecting implicit hate speech in online conversations. |
| Outcome: | The proposed framework outperforms baselines on 6 hate speech datasets and shows that it is highly efficient. |
Evaluating ChatGPT against Functionality Tests for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Large language models like ChatGPT have shown a great promise in detecting hate speech, but they lack the capability to perform in a holistic fashion. |
| Approach: | They evaluate the ChatGPT model's strengths and weaknesses by performing functional tests across 11 languages to uncover their weaknesses. |
| Outcome: | The proposed model performs poorly across 11 languages and is based on functional tests. |
Towards Explainable Hate Speech Detection (2025.findings-acl)
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| Challenge: | Recent advances in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. |
| Approach: | They propose a model that uses valence, arousal, and dominance (VAD) scores to detect hate speech and a weighted sum of valent, valance, and valency (VA) scores for classification. |
| Outcome: | The proposed model can compete with state-of-the-art models in detecting hate speech and non-hate speech words based on their individual and summed VAD-values. |