Computable Structures of National Narratives: A Dataset for Generating Governance Legitimacy Models Based on Computational Content Analysis, Emotional Mediation, and Semantic Networks

This dataset supports computational modelling research on national-level political narratives, employing the Computable Structure of National Narrative (CSNN) as its theoretical framework. It systematically demonstrates how national narratives generate governance legitimacy and social cohesion through structured content configuration, affective mediation mechanisms, and semantic network coupling. The dataset is constructed from publicly available national-level political texts, processed through computational content analysis, sentiment analysis, and semantic network analysis. This yields a multi-layered variable system encompassing narrative input variables (such as development, people's livelihoods, culture, discipline, and external governance narratives), mediating variables (emotional resonance, policy perceptibility, governance credibility), latent variables (national confidence, institutional trust), and outcome variables (governance legitimacy, social cohesion), with variable relationships explicitly defined through directed causal pathways. This dataset emphasises theory-driven, mechanism-oriented and reproducible methodologies, serving computational social science, political communication, governance studies, Text-as-Data approaches, and causal inference teaching, replication and extension research. All variable definitions, path specifications and data processing logic are explicitly documented in accompanying explanatory files, facilitating comparative analysis across texts, years and nations.