Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD

2026-09-07

AI Summary

研究通过对比302名参与者体验，探讨短视频推荐系统对ADHD用户的影响，揭示其导致的时间盲、后悔和情绪困扰问题，并提出神经多样性意识设计原则以减轻算法伤害。

Abstract

Short-form video platforms use recommender systems to maximize engagement through highly efficient personalized recommendations. However, the impact of these recommendations on users with ADHD compared to users without ADHD remains underexplored. Through this study, we introduce and operationalize the Engagement Trap, illustrating how recommender systems, while successfully optimizing for engagement, disproportionately disadvantage users with ADHD. This stratified study of 302 participants, recruited via the online platform Prolific, compares experiences between participants with and without ADHD. Our results show that while recommendations are perceived as relevant across groups, participants with ADHD report significantly higher levels of time blindness, post-usage regret, and emotional distress when consuming recommendations. Moreover, we collect feedback for several proof-of- concept, theoretical design interventions for neuro-inclusive design principles. These findings provide quantitative evidence of systemic differences in engagement-optimized recommender systems and highlight the unbalanced negative effects and interactions these systems create for participants with ADHD. We argue for neurodiversity-aware, human-centered design approaches that mitigate such algorithmic harms and support more equitable experiences.

Problem

• ADHD
• recommender systems
• engagement
• neurodiversity
• algorithmic harms

Innovation

• Engagement Trap
• ADHD
• recommender systems
• neuro-inclusive design