Content Moderation in Social Media: The Characteristics, Degree, and Efficiency of User Engagement

Kanlun Wang, Zhe Fu, Lina Zhou, Yunqin Zhu
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Abstract

Social media emerge as common platforms for knowledge sharing/exchange in online communities. Meanwhile, they also become a hotbed for the diffusion of misinformation. Content moderation is one of the measures for preventing the distribution of misinformation. Despite the increasing research attention to content moderation, the role of user engagement in content moderation remains significantly understudied. It is unclear how different characteristics and degrees of user engagement in social media might impact the performance of content moderation. In addition, the efficiency of content moderation has not been addressed by prior studies. This study aims to fill these research gaps by investigating the characteristics of user engagement behavior in social media and developing automated models to support content moderation that leverage a state-of-the-art pre-trained model for text analysis. The evaluation results with Reddit data suggest that the directivity and temporal characteristics of user engagement have significant effects on the effectiveness of content moderation. Additionally, leveraging the entire history of user engagement tends to be inefficient or even impractical, yet our findings provide evidence and a guide for improving the efficiency of content moderation using user engagement data without compromising model effectiveness. Our findings have research and practical implications for the moderation and deterrence of misinformation in social media.
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社交媒体中的内容审核:用户参与的特征、程度和效率
社交媒体成为在线社区知识共享/交流的通用平台。与此同时,它们也成为传播错误信息的温床。内容审核是防止虚假信息传播的措施之一。尽管对内容审核的研究越来越多,但用户参与在内容审核中的作用仍未得到充分研究。目前尚不清楚用户参与社交媒体的不同特征和程度如何影响内容审核的表现。此外,先前的研究并未涉及内容审核的效率。本研究旨在通过调查社交媒体中用户参与行为的特征,并开发自动化模型来支持内容审核,利用最先进的预训练模型进行文本分析,从而填补这些研究空白。基于Reddit数据的评价结果表明,用户参与的指向性和时间特征对内容审核的有效性有显著影响。此外,利用用户参与度的整个历史往往效率低下甚至不切实际,但我们的研究结果为在不影响模型有效性的情况下使用用户参与度数据提高内容审核的效率提供了证据和指南。我们的研究结果对社交媒体上错误信息的节制和威慑具有研究和实际意义。
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