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

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.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.07795 [cs.HC]

(or arXiv:2609.07795v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.07795 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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