Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
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| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
| Approach: | They propose to replace existing datasets in English with synthetic data by rewriting original texts with an instruction-based generative model. |
| Outcome: | The proposed model improves performance in cross-dataset training. |
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| Challenge: | Detecting online abusive language in social media messages is gaining increasing attention from scholars and stakeholders. |
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Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It? (2023.eacl-main)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
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Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
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| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
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Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
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| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
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