Filtering Offensive Content Changes Its Visibility but Not User Behavior: Two Randomized Controlled Trials with 200,000 Users on Nextdoor
We investigate the effectiveness of interventions that reduce the visibility of offensive content on the local social platform Nextdoor. Content filtering -- hiding or downranking offensive content that brushes against a platform's rules without clearly breaking them -- is deployed across virtually every major platform, yet almost no field evidence exists on whether it changes user behavior. We report two large-scale randomized controlled trials, each involving 100,000 users. Study 1 (2022) tested a report-triggered filter applied to comments in post threads and produced a modest 12% reduction in views of offensive comments; across eleven further measures of platform behavior we found no significant effects. Study 2 (2023-2024) remedied Study 1's central limitation -- a weak manipulation driven by slow, report-based eligibility -- by proactively scoring posts and comments at creation with Google Jigsaw's Perspective API and filtering them from the newsfeed. This produced a near-complete (95%) reduction in views of offensive posts, yet across thirteen further measures we again found no significant effects. Across two independent trials spanning different content types, filtering mechanisms, classifiers, and countries -- and despite manipulation strength rising from 12% to 95% -- filtering reliably reduced the visibility of offensive content without altering platform visitation, content consumption, or content production. These convergent null results provide rare field evidence on a ubiquitous intervention and underscore the complexity of effectively moderating online platforms.