| Challenge: | Hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. |
| Approach: | They propose a suite of functional tests for hate speech detection models that measure model performance on held-out test data and then craft test cases to validate their quality. |
| Outcome: | The proposed tests show that the proposed models perform poorly on a small set of widely-used hate speech datasets. |
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| Challenge: | HateCheck test cases are generic and have simplistic sentence structures that do not match the real-world data. |
| Approach: | They propose a framework to generate more diverse and realistic functional tests from scratch by instructing large language models. |
| Outcome: | The proposed framework generates more diverse and realistic functional tests from scratch by instructing large language models (LLMs). |
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models (2022.lrec-1)
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| Challenge: | Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses. |
| Approach: | They propose to use multilingual hate speech detection models to evaluate their performance on social media conversation. |
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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. |
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Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate (2022.naacl-main)
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| Challenge: | Existing models for detecting hate expressed with emojis have weaknesses when used for sensitive applications such as content moderation. |
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| Challenge: | Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP. |
| Approach: | They argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task. |
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HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter (2025.acl-long)
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Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale, Samuel Fraiberger, Victor Orozco-Olvera, Paul Röttger
| Challenge: | Prior work on automated hate speech detection models has been limited due to systematic biases in evaluation datasets and poor performance across geographies. |
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Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)
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| Challenge: | Existing methods that focus on a single tweet as input are likely to yield high false positive and negative rates. |
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RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection (2026.acl-long)
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| Challenge: | a new framework for hate speech detection addresses implicit hate speech by tailoring the detection process to dataset-specific attributes. |
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Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
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| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
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HateModerate: Testing Hate Speech Detectors against Content Moderation Policies (2024.findings-naacl)
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| Challenge: | Existing studies on hate speech detection have failed to answer this question. |
| Approach: | They propose a dataset for testing the behaviors of automated content moderators against content policies. |
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