The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit

TL;DR

The paper evaluates The Great Ban, a massive deplatforming of nearly 2,000 Reddit communities, by analyzing 16 million comments from around 17,000 users over 14 months to measure toxicity changes and unintended outcomes. Using Detoxify toxicity scores, validated against Perspective API, it conducts user- and subreddit'scale analyses across 15 popular banned subreddits to assess both overall effectiveness and extreme reactions. The findings show a modest overall toxicity reduction among remaining users ($ ext{Δtoxicity} \,\approx \, -$ $6.57\\%$) but with a notable minority (about $5\%$) increasing toxicity by more than $70\%$ of their pre-ban level, and $15.6\%$ of users abandoning Reddit. These results imply displacement risks and heterogeneous responses to large-scale moderation, underscoring the need for calibrated, potentially personalized strategies that balance safety with minimizing adverse side effects. The work advances moderation science by providing a granular, user-centered view of deplatforming outcomes and outlining avenues for causal inference and predictive, tailored interventions.

Abstract

In the current landscape of online abuses and harms, effective content moderation is necessary to cultivate safe and inclusive online spaces. Yet, the effectiveness of many moderation interventions is still unclear. Here, we assess the effectiveness of The Great Ban, a massive deplatforming operation that affected nearly 2,000 communities on Reddit. By analyzing 16M comments posted by 17K users during 14 months, we provide nuanced results on the effects, both desired and otherwise, of the ban. Among our main findings is that 15.6% of the affected users left Reddit and that those who remained reduced their toxicity by 6.6% on average. The ban also caused 5% users to increase their toxicity by more than 70% of their pre-ban level. Overall, our multifaceted results provide new insights into the efficacy of deplatforming. As such, our findings can inform the development of future moderation interventions and the policing of online platforms.

Figures (6)

• Figure 1: Timeline depicting the periods of data collection and analysis. Collected data spans two time periods of 7 months each, centered around The Great Ban. It is noteworthy that the IN-BEFORE dataset has no content since May 2020, indicating that the activity in the banned subreddits halted before the official intervention date.
• Figure 2: Comparison between the toxicity (left panel) and severe toxicity (right panel) indicators provided by Perspective API (x axis) and Detoxify (y axis). Overall, we report a strong Pearson correlation between the two methods, and particularly so for the toxicity indicator ($r = 0.872$).
• Figure 3: User-level toxicity change after The Great Ban, for each active user. The slope chart in the central panel reveals a majority of red-colored rising lines, corresponding to a large number of users who drastically increased their toxicity. The beeswarm plot in the bottom panel confirms this effect, presenting more users in the right red-colored tail of the distribution than in the left blue-colored one. Boxplots present marginal distributions for abandoning and remaining user, before and after the intervention.
• Figure 4: User-level toxicity changes for participants in r/the_donald (top) and r/consumeproduct (bottom).
• Figure 5: Subreddit-wise changes in toxicity obtained by summing all individual decreasing and increasing contributions, for all users (left panel) and outlier users (right panel). Outlier users are those whose change in toxicity exceeds the threshold $k = 0.25$. Based on this criterion, we found no outliers for r/hatecrimehoaxes, which is thus omitted from the right panel figure.