Elisabeth Lex

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.

Online media shapes public opinion, with messages from opposing information sources deepening polarization. How content is framed and presented determines its meaning and impact on readers. To date, studies on framing have focused on finding certain phrases, topics, or ideas characterizing messages from particular sources, a strategy providing only limited results. In this study, we used a broader, agnostic approach that involves extracting the narrative from text, including its characters, plot, setting, and moral of the story, and the relationships between these elements. We translated text from tens of thousands of health documents in the language of conspiracy corpus (LOCO) into representations called semantic graphs . We then compared these graphs across documents from conspiracy and mainstream sources. We found that conspiracy media framed health through belief, emphasizing immediacy and individual impact, whereas mainstream media used scientific framing with a long-term, institutional focus. Shared words carried divergent narratives. As one example, in documents related to COVID-19, conspiracy media emphasized preventing violence rather than preventing infection (the mainstream narrative). Understanding conspiracy narratives is crucial for policymakers and media platforms seeking to curb their spread. This understanding can inform efforts to boost media literacy and reduce the number of posts peddling misinformation. We suggest using automated tools and AI as an aid in both efforts.