Understanding the Gap Between Stated and Revealed Preferences in News Curation: A Study of Young Adult Social Media Users

2026-04-13

AI Summary

This study addresses the misalignment between social media algorithms’ stated values and users’ actual interactions, which often results in recommendations that diverge from users’ genuine informational needs—particularly among young adults. Employing a mixed-methods approach combining surveys, in-depth interviews, and a participatory news curation experiment, the research introduces user curation behavior as a novel mechanism for uncovering preference gaps. Findings reveal that while users passively encounter low-quality content, their active curation yields news feeds that are more accurate, diverse, and satisfying. The study conceptualizes curation as a value-laden process embedded in social relationships and contextual factors, thereby proposing a new pathway for algorithmic design grounded in normative principles such as accuracy and diversity.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Learning Preferences or Rankings

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Social media feed algorithms infer user preferences from their past behaviors. Yet what drives engagement often diverges from what users value. We examine this gap between stated preferences (what users say they prefer) and revealed preferences (what their behavior suggests they prefer) among young adults, a group deeply embedded in algorithmically mediated environments. Using a mixed-methods approach combining surveys and interviews with feed curation activities, we investigate: what gaps exist between stated and revealed preferences; how users make sense of these gaps; what values users believe should guide algorithmic curation; and how systems might reflect those values. Participants often found themselves engaging with low-quality content they did not endorse, despite wanting high-quality information. When asked to curate an ideal social media news feed for a hypothetical persona, participants created feeds they considered more satisfying and higher in quality by prioritizing values such as accuracy and diversity. In doing so, they navigated trade-offs between different values, factoring in social relationships and context surrounding the persona. These findings suggest that feed curation is a socially situated process of judging what should be visible and appropriate in shared information spaces. Based on these insights, we offer design directions for bridging the gap between stated and revealed preferences.

Problem

• stated preferences
• revealed preferences
• news curation
• algorithmic bias
• social media

Innovation

• stated vs. revealed preferences
• algorithmic curation
• value-sensitive design
• social media news feed
• mixed-methods study