ABSTRACT This study presents one of the first comparative analyses of digital nationalism on social media. Using a computational mixed‐method approach—combining supervised, computer‐assisted content analysis with network modelling—it analyses 64,541 tweets from Twitter and 91,063 posts from Weibo surrounding a shared geopolitical flashpoint: President Trump's blaming of China during the early stages of the COVID‐19 pandemic. The analysis reveals not only divergent manifestations of nationalism over time but also distinct user roles and patterns of discursive engagement that exemplify contrasting sociopolitical contexts. Additionally, we identify less discussed cross‐platform dynamics. The study makes three key contributions to the evolving field of digital nationalism. Empirically, it offers a fine‐grained, longitudinal mapping of everyday nationalist expressions by diverse actors in specific sociopolitical contexts over a 9‐month period (March–December 2020), capturing both temporal dynamics and nonelite perspectives. Methodologically, it advances a context‐sensitive, inductive–deductive analytic strategy that supports the identification and comparison of nationalist discourses and the abstraction of the contextual conditions under which they emerge and circulate. Theoretically, the study deepens our understanding of digital nationalism as a contingent phenomenon, co‐produced through the interplay of political cultures, user agency and media ecosystems—thus advancing a comparative framework attuned to the sociotechnical dynamics of digital nationalism.
Su et al. (Sat,) studied this question.