平台可见性机制与知识权威重构——以微信读书为例

Platform Visibility Mechanisms and the Reconstruction of Knowledge Authority—A Case Study of WeChat Reading

摘要: 数字阅读平台的兴起使普通读者的阅读行为从私人体验转变为可共享的公共文本，重塑了知识生产与传播的权威格局。本研究以微信读书为案例，结合网络化把关与算法策展理论，采用内容分析与平台功能观察相结合的方法，系统考察了“热门划线”与“高赞想法”两种可见性机制如何筛选和放大特定类型的知识表达，进而重构知识权威。研究发现，热门划线奉行“金句逻辑”，以去语境化、论断性强的文本片段为主；高赞想法奉行“共鸣逻辑”，以高情感浓度、易迁移的个人经验为主。两者与专业书评的筛选标准存在结构性偏差。平台算法与用户集体行为共同构成了一种“分布式算法把关”机制，使知识权威的生成基础从“专业认证”转向“数据认可”，知识的评价标准从真理性维度向可传播性维度滑动。这一发现修正了传统把关人理论在知识生产场景中的预设，揭示了把关行为从个体判断向算法聚合的转变，为理解数字平台环境下的知识权力转移提供了新的经验证据与理论解释。

Abstract: The rise of digital reading platforms has transformed ordinary readers’ reading behavior from a private experience into sharable public texts, reshaping the authority structure of knowledge production and dissemination. Taking WeChat Reading as a case study, this research integrates network gatekeeping theory and algorithmic curation theory, employing content analysis combined with platform function observation to systematically examine how two visibility mechanisms “popular highlights” and “top-liked thoughts” select and amplify specific types of knowledge expression, thereby reconstructing knowledge authority. The findings reveal that popular highlights follow a “soundbite logic”, predominantly presenting decontextualized, assertive textual fragments, while top-liked thoughts follow a “resonance logic”, primarily featuring emotionally charged and easily transferable personal experiences. Both exhibit structural deviations from the screening criteria of professional book reviews. Platform algorithms and collective user behavior jointly constitute a “distributed algorithmic gatekeeping” mechanism, shifting the foundation of knowledge authority from “professional certification” to “data recognition” and sliding the evaluation criteria of knowledge from the dimension of truth toward the dimension of communicability. This finding revises the presuppositions of traditional gatekeeping theory in the context of knowledge production, revealing the transformation of gatekeeping from individual judgment to algorithmic aggregation, and providing new empirical evidence and theoretical explanation for understanding the transfer of knowledge power in the digital platform environment.