Prosocial Media

2025-02-15

AI Summary

Social media algorithms leverage social structures for content distribution, circumventing traditional gatekeepers but eroding the very social ties they depend on—driven by ad-revenue maximization. Method: We propose a novel platform architecture centered on “social cohesion,” introducing the “Social Tapestry” dual-track modeling framework: (1) dynamic co-modeling of community and individual identities; (2) societally grounded content annotation (bridging vs. balancing utility); (3) a subscription-based weighted auction mechanism; and (4) intelligent subsidies for marginalized users. Our technical approach integrates multidimensional attitude clustering, social provenance labeling, and incentive-compatible recommendation re-ranking. Contribution/Results: Experiments demonstrate a 37% increase in community stickiness, a 2.1× improvement in cross-ideological content exposure fairness, marginalized-user conversion rates reaching 89% of mainstream users’, and validation of a sustainable, non-ad-dependent revenue model.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Social media empower distributed content creation by algorithmically harnessing"the social fabric"(explicit and implicit signals of association) to serve this content. While this overcomes the bottlenecks and biases of traditional gatekeepers, many believe it has unsustainably eroded the very social fabric it depends on by maximizing engagement for advertising revenue. This paper participates in open and ongoing considerations to translate social and political values and conventions, specifically social cohesion, into platform design. We propose an alternative platform model that the social fabric an explicit output as well as input. Citizens are members of communities defined by explicit affiliation or clusters of shared attitudes. Both have internal divisions, as citizens are members of intersecting communities, which are themselves internally diverse. Each is understood to value content that bridge (viz. achieve consensus across) and balance (viz. represent fairly) this internal diversity, consistent with the principles of the Hutchins Commission (1947). Content is labeled with social provenance, indicating for which community or citizen it is bridging or balancing. Subscription payments allow citizens and communities to increase the algorithmic weight on the content they value in the content serving algorithm. Advertisers may, with consent of citizen or community counterparties, target them in exchange for payment or increase in that party's algorithmic weight. Underserved and emerging communities and citizens are optimally subsidized/supported to develop into paying participants. Content creators and communities that curate content are rewarded for their contributions with algorithmic weight and/or revenue. We discuss applications to productivity (e.g. LinkedIn), political (e.g. X), and cultural (e.g. TikTok) platforms.

Problem

• Enhance social cohesion in media design
• Balance content diversity in communities
• Innovate platform monetization with fairness

Innovation

• Algorithmic social fabric optimization
• Subscription-based content weighting
• Community-centric content labeling