Comparative Analysis of Social Media News Framing Across Nations

Thesis Abstract

In an era of rapid digital information flows, social media has emerged as a pivotal arena where news is framed, disseminated, and interpreted across diverse national contexts, yet cross-national differences in framing practices remain inadequately understood and operationalized for comparative analysis. This study investigates how major social media platforms shape news framing across nations, examining the extent to which platform affordances, cultural norms, and political communication ecosystems influence frame construction, agenda emphasis, and interpretive cues in user engagement. The aim is to elucidate systematic variations in framing practices and their implications for public understanding, political perception, and media literacy across socio-political systems. The specific objectives are (1) to identify dominant frame families (episodic, thematic, conflict, human-interest, and morality frames) used in social media news posts by leading national outlets in three representative countries from different media-regulatory regimes; (2) to compare frame deployment across platforms (Twitter, Facebook, and Instagram) and examine the moderating role of platform affordances on framing choices; (3) to assess audience reception signals, including comments sentiment, shares, and engagement patterns, as proxies for perceived frame resonance; (4) to test theoretical propositions from Framing Theory and Cultural-Sociological Frame Alignment to determine whether cross-national differences align with national media systems theory and political parallelism; and (5) to propose a cross-national model of social media news framing that integrates platform dynamics with macro-level institutional factors. The study adopts a comparative cross-sectional design combining content analysis and mixed-methods audience analytics. The population comprises English-language news posts from the official social media accounts of leading national outlets in Country A, Country B, and Country C, during a six-month period (January–June 2025). A stratified random sample of 600 posts per country (total N = 1,800) will be collected across Twitter (now X), Facebook, and Instagram, ensuring proportional representation by platform and outlet tier. Content analysis coding will classify frames, topics, source attribution, and tone, using a coding scheme informed by Entman’s framing components (problem definition, causal explanation, moral evaluation) and Reese, Gandy, and Grant’s frame families. Intercoder reliability will be established with Cohen’s kappa exceeding 0.80. Audience engagement data (likes, shares, comments, and sentiment scores derived from a validated machine-learning classifier) will be extracted to gauge frame resonance. To triangulate quantitative findings, a qualitative thematic analysis of 150 representative comments per country will explore interpretive repertoires and affective responses to predominant frames. Statistical analyses will include chi-square tests to compare frame frequencies across countries and platforms, multivariate logistic regression to identify predictors of specific frame adoption, and ANOVA to assess differences in engagement metrics by frame type and country. A multilevel modeling approach will account for nested data (posts within outlets within countries). The theoretical framework integrates Framing Theory with the Political Power and Media System Theory, complemented by the Cultural-Sociological Frame Alignment perspective, allowing examination of how national norms, media ownership, and regulatory environments condition cross-national framing patterns. Expected findings include discernible differences in episodic versus thematic framing prevalence, varying reliance on moral and conflict frames aligned with national political cultures, and platform-specific moderation of framing intensity and sentiment. It is anticipated that more liberal-democratic systems exhibit greater thematic framing and transparent attribution, while more centralized or partisan systems display stronger episodic framing and source-framing emphasis. Platform affordances are expected to influence frame granularity and immediacy, with Twitter posts showing tighter problem definitions and faster normative cues, compared with Instagram’s visual-centric framing that amplifies moral and human-interest frames. The study contributes to knowledge by offering a rigorous, empirical cross-national model of social media news framing, detailing how platform ecosystems interact with national media structures to shape public discourse. It advances Framing Theory in a digital, cross-cultural context and informs policymakers, media practitioners, and educators about how framing choices may affect public opinion formation and media literacy. Practical implications include recommendations for newsroom framing guidelines that enhance transparency, reduce misinterpretation, and promote audience trust across diverse digital environments. The conclusion will highlight the robustness of cross-national framing patterns, identify context-specific nuances, and propose avenues for longitudinal follow-up to capture dynamic shifts in the evolving social media news ecosystem.