“It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online (2022.emnlp-main)
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| Challenge: | Detecting offensive content is becoming a critical task in natural language processing . but most datasets use a single binary label for hate or incivility, even though each concept is multi-faceted . a more fine-grained multi-label approach addresses conceptual and performance issues . |
| Approach: | They propose to use a dataset to annotate offensive online speech with six labels . they propose to apply a more fine-grained approach to predicting incivility and hateful content . |
| Outcome: | The proposed approach outperforms or matches benchmark datasets on the annotated tweets. |
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| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
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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. |
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| Challenge: | Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators. |
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| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
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Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021.acl-long)
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| Challenge: | Detecting online hate speech has proven difficult and concerns raised about performance, robustness, generalisability and fairness of stateof-the-art models. |
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| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
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Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)
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| Challenge: | Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP. |
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| Challenge: | Existing datasets for hate speech detection neglect the cultural diversity within a single language. |
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When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (2026.eacl-short)
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| Challenge: | Existing methods for hate speech detection treat hate speech as a monolithic phenomenon, ignoring the speaker’s motivations and potential societal consequences. |
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