2024/10/04 by Christopher Kelly, Kelly, Christopher, Jonatan Fontanez +3 · 1 citation
Social Sciences · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multimedia Communication and Technology
paper · pdf · doi:10.48550/arxiv.2410.03866
openalex publication_date 2024/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Search engine results often misalign with users' goals due to opaque algorithms, leading to unhelpful or detrimental information consumption. To address this, we developed a Google Chrome plugin that provides "content labels" for webpages in Google search results, assessing Actionability (guiding actions), Knowledge (enhancing understanding), and Emotion. Using natural language processing and machine learning, the plugin predicts these properties from webpage text based on models trained on participants' ratings, effectively reflecting user perceptions. The implications include enhanced user control over information consumption and promotion of healthier engagement with online content, potentially improving decision-making and well-being.