Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems for Social Media

Abstract

Social media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X's Community Notes. These systems -- which we term Crowdsourced Context Systems (CCS) -- have the potential to reshape the information ecosystem as major platforms embrace them as alternatives to professional fact-checking. To understand the features and implications of these systems, we conduct a systematic literature review of existing CCS research (n=56) and analyze real-world CCS implementations. Based on our analysis, we develop a framework with two components. First, we present a theoretical model to conceptualize and define CCS. Second, we identify a design space encompassing six aspects: participation, inputs, curation, presentation, platform treatment, and transparency. We also surface normative implications of different CCS design and implementation choices. Our work integrates theoretical, design, and ethical perspectives to establish a foundation for future human-centered research on Crowdsourced Context Systems.

Forthcoming at ACM CHI 2026

References in corpus (24)

• Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions
• A Design Space for Intelligent and Interactive Writing Assistants
• A Framework of Severity for Harmful Content Online
• Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
• Did the Roll-Out of Community Notes Reduce Engagement With Misinformation on X/Twitter?
• Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation
• What Do We Mean When We Talk about Trust in Social Media? A Systematic Review
• Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media
• Form-From: A Design Space of Social Media Systems
• Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance
• Community notes reduce engagement with and diffusion of false information online
• Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
• AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content
• Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility
• Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation
• Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media
• "There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit
• Community-based fact-checking reduces the spread of misleading posts on social media
• The Collaborative Practices and Motivations of Online Communities Dedicated to Voluntary Misinformation Response
• Collaboratively adding context to social media posts reduces the sharing of false news
• Community Notes vs. Snoping: How the Crowd Selects Fact-Checking Targets on Social Media
• AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments
• Rescuing Counterspeech: A Bridging-Based Approach to Combating Misinformation
• References to unbiased sources increase the helpfulness of community fact-checks