Community by Design

TL;DR

The paper tackles how to redesign social media to strengthen the social fabric rather than erode it, by modeling communities and citizens as a social hypergraph and making social provenance an explicit content attribute. It introduces a coherent pluralism framework grounded in Hutchins Commission principles, data dignity, and metamodernity, and details a mechanism consisting of a looping social-technical-economics system with enrollment, parametrization, and a multi-lens specification. Key contributions include the formalization of bridging and balancing content, a ranking formula that incorporates community interests, and an economic scheme where communities and citizens influence content weight through subscriptions and targeted advertising, all while addressing privacy, portability, accountability, and governance. The proposed approach aims to enable productive, political, and cultural platforms to foster common understanding, cross-cutting collaboration, and collective action in an increasingly automated world, potentially serving as a resilient public infrastructure for democratic deliberation and social cohesion.

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 includes 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.

Figures (5)

• Figure 1: Summary of three broad, antisocial pathologies of the public sphere, with our corresponding recommendations for the design of social media.
• Figure 2: Illustration of (a) the core business model for (most) existing social media platforms, and (b) our proposed business model.
• Figure 3: Diagram of the social media platform with participants, enrollment, algorithmic parametrization and loop of actions.
• Figure 4: Illustration of how social provenance and context could be made transparent.
• Figure 5: Illustration of how an LLM could be used to summarize the points a community makes about a given topic, and how these compare to adjacent communities