A Convergent Perspective on Policy Communication on Social Media: A Mixed-Methods Approach Using Text Mining and Social Network Analysis

Social media is a vital platform for policy communication, yet research rarely integrates content and structural features to evaluate communication effects multidimensionally. This study proposes a convergent framework combining text mining and social network analysis to assess policy communication effects and underlying mechanisms. Using China’s upgraded Mass Entrepreneurship and Innovation policy on Sina Weibo as a case, we analyze communication breadth, depth, audience sentiment, thematic focus, network topology, key nodes, and community characteristics. The results reveal that (1) communication breadth is dominated by official communicators, while audiences drive interactive depth, reflecting a “centralized broadcasting, decentralized engagement” model; (2) influential users express more positive attitudes than ordinary audiences; (3) discussions diversify from core innovation themes to micro-level concerns like regional development and talent policies; (4) the network shows loose global structure but strong local clustering, with bridging nodes posting less polarized, broader content. Theoretically, this study offers behavioral-level observations that align with key corollaries of the Spiral of Silence Theory—the tendency for individuals with deviating views to shift toward lower-visibility participation. These pattern-level findings offer a complementary empirical perspective on opinion expression in digital policy contexts. Practically, the findings offer preliminary insights that may inform adaptive, decentralized strategies for enhancing policy diffusion in similar social media contexts.