🤖 AI Summary
This study addresses the polarization, misinformation, and incivility exacerbated by mainstream social platforms’ excessive pursuit of engagement. It proposes an interdisciplinary system design framework centered on democratic values rather than mere engagement metrics. By integrating modular mechanisms—including open recommender systems, AI-assisted fact-checking, cognitive feedback loops, and reputation systems—this work constructs a design menu for social media that fosters exposure to diverse perspectives and high-quality participation. Moving beyond traditional engagement-driven paradigms, it offers both a systematic technical blueprint and theoretical foundations for reconstructing digital public spaces that support democratic discourse and collective intelligence.
📝 Abstract
Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenue, contributing to concerns about polarization, misinformation, social isolation, and loss of civility. We explore how social media might instead be deliberately designed to support democratic discourse, collective deliberation, and collective intelligence. Drawing on literature across computer science, psychology, political science, and related fields, we present a modular collection of mechanisms that could be implemented individually or in combination. These include user-controlled and open recommender systems, tools for exposure to diverse perspectives, new forms of cognitive and epistemic feedback, collaborative and AI-assisted fact-checking, privacy and visibility controls, mechanisms for improving civility and evidentiary integrity, and reputation systems that reward high-quality participation. The proposals are intended both as a practical menu of design possibilities and as a starting point for broader interdisciplinary discussion about digital public spaces designed around democratic values rather than engagement alone.