Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor
Shagun Jhaver, Alice Qian Zhang, Quanze Chen, Nikhila Natarajan, Ruotong Wang, Amy Zhang

TL;DR
This study explores social media users' perceptions of personal content moderation tools, their preferences, and the impact on perceptions of platform responsibility, through interviews and simulated interface interactions.
Contribution
It provides novel insights into user attitudes towards personalized moderation tools and discusses design implications for enhancing transparency and control.
Findings
Users value transparency and control in moderation tools.
Personalization influences perceptions of platform responsibility.
Users are willing to engage labor for better moderation control.
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'…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Social Media and Politics
