Content Removal as a Moderation Strategy

Abstract

Moderators of online communities often employ comment deletion as a tool. We ask here whether, beyond the positive effects of shielding a community from undesirable content, does comment removal actually cause the behavior of the comment's author to improve? We examine this question in a particularly well-moderated community, the ChangeMyView subreddit.The standard analytic approach of interrupted time-series analysis unfortunately cannot answer this question of causality because it fails to distinguish the effect of having made a non-compliant comment from the effect of being subjected to moderator removal of that comment. We therefore leverage a "delayed feedback" approach based on the observation that some users may remain active between the time when they posted the non-compliant comment and the time when that comment is deleted. Applying this approach to such users, we reveal the causal role of comment deletion in reducing immediate noncompliance rates, although we do not find evidence of it having a causal role in inducing other behavior improvements. Our work thus empirically demonstrates both the promise and some potential limits of content removal as a positive moderation strategy, and points to future directions for identifying causal effects from observational data.

Measuring Toxicity Toward Women in Game-Based Communities

“…Good moderation is capable of warning users ahead of time in a stickied thread that toxic comments will not be tolerated, directly removing toxic comments, and banning frequent offenders. The results of a study by Srinivasan et al (2019) showed that effective moderation, although not a significant contributor to future reformed behaviors, did significantly contribute to reduced future rates of toxic comments.…”

Moderation Visibility: Mapping the Strategies of Volunteer Moderators in Live Streaming Micro Communities

“…We echoed some moderation strategies broadly applied in online spaces such as content removal and banning the end-user [31,54]. However, most of these common strategies are working separately.…”

You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok

“…For example, deplatformization seems effective to stem toxic communication (Jhaver et al, 2021), while limiting access to toxic communities slowed down new member recruitment but did not affect established toxic communication within the community (Chandrasekharan et al, 2022). Content removal may effectively restore platform guideline compliance, yet it does not appear to affect overall platform behavior (Srinivasan et al, 2019). One solution might be a collaborative content moderation between AI and humans (Lai et al, 2022), mainly because AI-based content moderation on social media appears promising only from a technological perspective (Gillespie, 2020) but remains intransparent (Suzor et al, 2019), hard to understand, and largely unable to capture communicative context on the user side (Gorwa et al, 2020).…”