Exploring COVID-19 Framing Across Diverse Platforms: Analyzing Semantic and Contextual Shifts in Public Discussion, News Media, and Government Communication

Abstract

Understanding how shared issues are framed differently across public, news, and government discourse is central to the study of COVID-19 communication. This paper uses the previously validated Linked Latent Theta Role (LLTR) model as part of a novel analytic technique to examine framing differences across Reddit posts, mainstream news articles, and state public health bulletins. Rather than introducing a new model, we operationalize LLTR outputs to compare cross-source framing by examining how shared topic words are embedded in different syntactic constructions. Using a source-balanced corpus, we measure cross-source differences using Jensen–Shannon divergence over distributions of dependency-based relation–argument pairs, and contrast these results with a lexical baseline. Across eight COVID-19 topics, we find that sources often rely on overlapping topic vocabularies, yet diverge substantially in their syntactic realizations of those topics. Inspection of high-divergence grammatical evidence reveals systematic differences in how sources assign agency, attribute responsibility, and structure evaluative context around shared topical concepts. These findings suggest that grammar-aware representations provide an interpretable and scalable basis for identifying framing differences that are not visible at the lexical level alone.