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Let Community Rules Be Reflected in Online Content Moderation

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arxiv 2408.12035 v1 pith:UACPWSQT submitted 2024-08-21 cs.SI cs.CLcs.LGcs.MM

classification cs.SIcs.CLcs.LGcs.MM
keywords contentmoderationcommunitymodelsrulesonlineresearchcommunities
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison

    eess.IV 2024-12 reject novelty 4.0 of 10

    Using an AI judge, the authors report that general-purpose language models produce diagnoses preferred over physician reports in up to 85% of augmented abdominal CT cases.

  2. DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning

    cs.CV 2024-11 conditional novelty 4.0 of 10

    DyConfidMatch sets per-class pseudo-label thresholds and re-sampling weights from class-level confidence, improving semi-supervised 3D classification and detection.

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