Research

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

Personal moderation tools
Transparency and controllability
Labor challenges

Shagun Jhaver, Alice Qian Zhang, Quan Ze Chan, Nikhila Natarajan, Ruotong Wang, and Amy X. Zhang (2023), “Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor,” Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 289 (October 2023), 33 pages, DOI: 10.1145/3610080

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.

BibTeX citation

@article{Jhaver:2023Personalizing,
    author = {Jhaver, Shagun and Zhang, Alice Qian and Chan, Quan Ze and Natarajan, Nikhila and Wang, Ruotong, and Zhang, Amy X.}, 
    title = {Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor}, 
    year = {2023}, 
    publisher = {Association for Computing Machinery}, 
    address = {New York, NY, USA}, 
    journal = {Proc. ACM Hum.-Comput. Interact.}, 
    numpages = {33}, 
    }