Reducing the rate of personal insults in social media with bystander bots

2026-06-18

AI Summary

Online social platforms are increasingly plagued by personal insults. This study addresses this issue by conducting a large-scale randomized controlled trial on Reddit to systematically evaluate the effectiveness of five natural language generation–based automated bystander intervention strategies in reducing users’ subsequent aggressive speech. The research deployed automated comment bots that respond in real time to offensive content with de-escalation messages. Results demonstrate that automated interventions significantly reduced insulting behavior overall, with the “expressing appreciation” strategy yielding the strongest effect, while certain other strategies showed no significant impact—highlighting critical nuances in intervention design. This work establishes the feasibility and heterogeneous efficacy of automated de-escalation approaches, offering a novel paradigm for online content moderation.

Abstract

Prompted by previous research on strategies for reducing interpersonal conflict and addressing problematic behaviors in online communities, a randomized controlled trial on Reddit compared various responses for reducing the rate of personal insults users post to the site. We generated replies from five deescalation strategies and used an automated procedure for posting them as replies to insulting comments. The findings reveal that automated replies to insults can effectively reduce their rate. Appreciation performed best. Not all strategies performed well, though. We conclude that automated responses are a viable tool for addressing some problematic behaviors. We discuss their potential utility and limitations.

Problem

• personal insults
• social media
• bystander bots
• online communities
• problematic behaviors

Innovation

• bystander bots
• automated de-escalation
• online toxicity
• randomized controlled trial
• social media moderation