Leveraging AI for Direct Bystander Intervention Against Cyberbullying

2026-04-20

AI Summary

This study addresses the challenge that online bystanders often refrain from intervening in cyberbullying due to insufficient skills and low self-efficacy. To overcome this barrier, the authors propose EmojiGen—a lightweight AI-powered interactive tool that, for the first time, integrates emoji-based input (representing users’ intervention intentions) with context-aware natural language generation to automatically produce appropriate, situationally tailored responses to cyberbullying incidents. In a user study with 90 participants, EmojiGen significantly increased the frequency of direct bystander interventions—such as supporting victims or confronting perpetrators—while enhancing participants’ perceived capability to help others. Moreover, it effectively reduced intervention-related anxiety and cognitive load, offering a low-barrier, highly usable paradigm to foster proactive bystander behavior in online environments.

Technology Category

• Humans and AI: AI for Accessibility
• Intelligent Robots: Embodied AI
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews

Abstract

Cyberbullying is a pervasive problem in online environments, causing substantial psychological harm to victims. Although bystander intervention has proven effective in mitigating its impact, motivating bystanders to engage in direct intervention remains a persistent challenge. Studies have suggested that difficulties in intervention skills and defending self-efficacy hinder bystanders from initiating direct intervention. To address this challenge, we introduced EmojiGen, an AI intervention tool designed to empower bystanders for direct intervention. EmojiGen enabled users to simply select an emoji as an intention clue, which subsequently combined the cyberbullying context to generate responses. In a between-subjects experiment involving 90 participants on a custom-built social media platform, we found that EmojiGen significantly increased the frequency of direct bystander interventions, both in supporting victims and in confronting perpetrators, driven by different factors. EmojiGen also increased the sense of knowing how to help and defending self-efficacy, while reducing perceived workload and anxiety associated with initiating intervention. The study contributed to the CSCW community through offering an effective direct bystander intervention method and providing design implications for future cyberbullying interventions.

Problem

• cyberbullying
• bystander intervention
• self-efficacy
• intervention skills
• online harassment

Innovation

• AI intervention
• bystander empowerment
• EmojiGen
• cyberbullying mitigation
• self-efficacy