Work

Incorporating Procedural Fairness in Flag Submissions on Social Media Platforms

Year: 2026

Type: article

Abstract: Flagging mechanisms on social media platforms allow users to report inappropriate posts/accounts for review by content moderators. These reports are pivotal to platforms’ efforts toward regulating norm violations. This article examines how platforms’ design choices in implementing flagging mechanisms influence flaggers’ perceptions of content moderation. We conducted a survey experiment asking US respondents ( N =2,936) to flag inappropriate posts using one of 54 randomly assigned flagging implementations. After flagging, participants rated their fairness perceptions of the flag submission process along the dimensions of consistency, transparency, and voice (agency). We found that participants perceived greater transparency when flagging interfaces included community guidelines and greater voice when they incorporated a text box for open-ended feedback. Our qualitative analysis of open-ended responses highlights user needs for improved accessibility, educational support for reporting, and protections against false flags. We offer design recommendations for building fairer flagging systems without exacerbating the cognitive burden of submitting flags.

Source: ACM Transactions on Social Computing

DOI: 10.1145/3797820

Authors: Yunhee Shim, Shagun Jhaver

Institution: Rutgers, The State University of New Jersey

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Peace, justice, and strong institutions

Open Access status: hybrid

Funder: National Science Foundation