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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.12035 |
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| _version_ | 1866909293433847808 |
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| author | Xin, Wangjiaxuan Wang, Kanlun Fu, Zhe Zhou, Lina |
| author_facet | Xin, Wangjiaxuan Wang, Kanlun Fu, Zhe Zhou, Lina |
| contents | Content moderation is a widely used strategy to prevent the dissemination of irregular information on social media platforms. Despite extensive research on developing automated models to support decision-making in content moderation, there remains a notable scarcity of studies that integrate the rules of online communities into content moderation. This study addresses this gap by proposing a community rule-based content moderation framework that directly integrates community rules into the moderation of user-generated content. Our experiment results with datasets collected from two domains demonstrate the superior performance of models based on the framework to baseline models across all evaluation metrics. In particular, incorporating community rules substantially enhances model performance in content moderation. The findings of this research have significant research and practical implications for improving the effectiveness and generalizability of content moderation models in online communities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12035 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Let Community Rules Be Reflected in Online Content Moderation Xin, Wangjiaxuan Wang, Kanlun Fu, Zhe Zhou, Lina Social and Information Networks Computation and Language Machine Learning Multimedia Content moderation is a widely used strategy to prevent the dissemination of irregular information on social media platforms. Despite extensive research on developing automated models to support decision-making in content moderation, there remains a notable scarcity of studies that integrate the rules of online communities into content moderation. This study addresses this gap by proposing a community rule-based content moderation framework that directly integrates community rules into the moderation of user-generated content. Our experiment results with datasets collected from two domains demonstrate the superior performance of models based on the framework to baseline models across all evaluation metrics. In particular, incorporating community rules substantially enhances model performance in content moderation. The findings of this research have significant research and practical implications for improving the effectiveness and generalizability of content moderation models in online communities. |
| title | Let Community Rules Be Reflected in Online Content Moderation |
| topic | Social and Information Networks Computation and Language Machine Learning Multimedia |
| url | https://arxiv.org/abs/2408.12035 |