Bystanders of Online Moderation: Examining the Effects of Witnessing Post-Removal Explanations

TL;DR

This paper asks whether bystanders—users who witness post-removal explanations—alter their behavior, extending prior work that focused on sanctioned users. Using a quasi-experimental causal-inference framework on $Dec ext{-}2021$ to $Dec ext{-}2022$ Reddit data from $r/AskReddit$ and $r/science$, it shows that witnessing explanations increases bystander posting frequency and interactivity, with no consistent effect on post removals. The findings support Deterrence Theory in an online, bystander context and imply that investing in transparency can improve norms and engagement across large communities. Limitations include platform scope and observational design; future work should explore AI-generated explanations, cross-platform replication, and finer exposure measures to maximize the educational value of moderation actions.

Abstract

Prior research on transparency in content moderation has demonstrated the benefits of offering post-removal explanations to sanctioned users. In this paper, we examine whether the influence of such explanations transcends those who are moderated to the bystanders who witness such explanations. We conduct a quasi-experimental study on two popular Reddit communities (r/askreddit and r/science) by collecting their data spanning 13 months-a total of 85.5M posts made by 5.9M users. Our causal-inference analyses show that bystanders significantly increase their posting activity and interactivity levels as compared to their matched control set of users. Our findings suggest that explanations clarify and reinforce the social norms of online spaces, enhance community engagement, and benefit many more members than previously understood. We discuss the theoretical implications and design recommendations of this research, focusing on how investing more efforts in post-removal explanations can help build thriving online communities.

Figures (2)

• Figure 1: Examples of post-removals and explanations by a moderator on (a) r/AskReddit (here, the explanation is provided by the Automoderator), and (b) r/science (here, the explanation is provided by a human moderator).
• Figure 2: A schematic figure showing our causal-inference approach to analyze users' Reddit timeline.