Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor

May 17, 2023 Β· Declared Dead Β· πŸ› Proceedings of the ACM on Human-Computer Interaction

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Authors Shagun Jhaver, Alice Qian Zhang, Quanze Chen, Nikhila Natarajan, Ruotong Wang, Amy Zhang arXiv ID 2305.10374 Category cs.HC: Human-Computer Interaction Citations 103 Venue Proceedings of the ACM on Human-Computer Interaction Last Checked 4 months ago
Abstract
Social media platforms moderate content for each user by incorporating the outputs of both platform-wide content moderation systems and, in some cases, user-configured personal moderation preferences. However, it is unclear (1) how end users perceive the choices and affordances of different kinds of personal content moderation tools, and (2) how the introduction of personalization impacts user perceptions of platforms' content moderation responsibilities. This paper investigates end users' perspectives on personal content moderation tools by conducting an interview study with a diverse sample of 24 active social media users. We probe interviewees' preferences using simulated personal moderation interfaces, including word filters, sliders for toxicity levels, and boolean toxicity toggles. We also examine the labor involved for users in choosing moderation settings and present users' attitudes about the roles and responsibilities of social media platforms and other stakeholders towards moderation. We discuss how our findings can inform design solutions to improve transparency and controllability in personal content moderation tools.
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