Social media platforms shape public discourse through two fundamental design choices that naturally co-occur in any field investigation: platform architecture, which defines what types of actors exist and how they interact, and recommendation algorithm, which determines what content is surfaced to users. Using agent-based simulation, we orthogonally manipulate both factors, exploring four prototypical architectures -- tree (e.g., Reddit), layered hierarchy (e.g., Facebook), network (e.g., Twitter), and complete graph (e.g., TikTok) -- and two algorithms: chronological (LIFO) and popularity-based (Hot). Drawing on prior theory that identifies and ranks canonical system architectures in terms of their flexibility we hypothesize that algorithmic effects on information spread and quality should be largest on the most flexible platforms and smallest on the most constrained ones. We find strong confirmation of this prediction. On tree-like platforms like Reddit, the algorithm has no detectable effect on information spread and quality. On layered hierarchies and networks like Facebook and Twitter, respectively, the Hot algorithm has modest positive effects on both the spread of information and its quality. On complete structures like TikTok, the Hot algorithm leads to a winner-take-all dynamics that has strong negative effects on both information spread and quality, making the relation between content quality and popularity unpredictable. These findings imply that architectural considerations are more powerful levers than algorithmic interventions for the design of healthy online spaces and public discourse. Platform reform efforts focused exclusively on algorithm choice may be insufficient on architecturally unconstrained platforms and unnecessary on architecturally constrained ones.

翻译：社交媒体平台通过两种在实地调研中自然共现的基础设计选择来塑造公共话语：平台架构（定义存在哪些类型的行动者及其互动方式）和推荐算法（决定向用户展示何种内容）。通过基于智能体的模拟，我们正交操控这两个因素，探索四种原型架构——树状结构（如Reddit）、分层结构（如Facebook）、网络结构（如Twitter）和完全图结构（如TikTok）——以及两种算法：时间顺序（后进先出型）和流行度驱动（热门型）。借鉴先前理论中按灵活性对规范系统架构进行识别与排序的研究成果，我们假设：算法对信息传播与质量的影响在最具灵活性的平台上最大，在约束最强的平台上最小。研究结果强有力地验证了这一预测。在Reddit等树状平台上，算法对信息传播和质量无显著影响；在Facebook和Twitter等分层结构与网络平台上，热门算法对信息传播及其质量均有适度积极效应；而在TikTok等完全图结构上，热门算法引发赢家通吃的动态机制，对信息传播和质量产生强烈负面影响，使内容质量与流行度之间的关系变得不可预测。这些发现表明，在设计健康的网络空间和公共话语时，架构考量比算法干预更具杠杆效力。聚焦于算法选择的平台改革努力，在架构约束力弱的平台上可能效果不足，而在架构约束力强的平台上则可能并非必要。