Abstract
The emergence of social media platforms such as Twitter and Facebook is widely acknowledged to have fundamentally changed digital activism. Scholars remain divided, however, on the role of collective identity for social movements online, particularly within the debate between connective and collective action. This paper contributes to this debate by demonstrating that collective identity formation online is possible, thereby creating distinguishable social actors that can be sustained over time. When evaluating collective identity formation online, previous research focused either on cultural references shared by platform users or on interaction patterns among them. Foundational theories on collective identity, though, argue that both cultural and social dynamics must be examined together. This paper fills this research gap by drawing on the notion of “style” as introduced by Harrison White, which inherently cap-tures collective identity’s duality of culture and structure. We operationalize style based on socio-semantic network analysis, enabling the empirical assessment of collective identity formation online. We apply our style conceptualization to Querdenken (German for “lateral think-ing”)—Germany’s most successful movement mobilizing against COVID-19 measures on Twitter. Our findings reveal that Querdenken’s collective identity online materializes through recurring social and cultural patterns that persist independently of individual users. These patterns are sustained over time by a multitude of users, with some temporarily assuming emergent leadership roles, thereby significantly shaping Querdenken’s collective identity. We theo-rize our findings in relation to the existing literature, offering nine propositions to guide future research on the collective identity formation of social movements online.
DOI
10.17705/1jais.00951
Recommended Citation
Henn, Theresa; Tell, Sarah; Polenz, Julian; Kern, Thomas; and Posegga, Oliver, "In Search of a “Style:” Capturing the Collective Identity of Social Movements Based on Digital Trace Data" (2025). JAIS Preprints (Forthcoming). 196.
DOI: 10.17705/1jais.00951
Available at:
https://aisel.aisnet.org/jais_preprints/196