Paper Number
ICIS2025-2512
Paper Type
Complete
Abstract
This study investigates how different types of language shape collective identity in digital protest movements. Using Instagram data from Brazil’s 2022 post-election unrest, we apply a computational framework that categorizes language into conative, referential, emotive, and expressive functions. Our analysis shows that referential and emotive language strongly predict the rise of collective identity. Conative language plays a transient role in the pro-government campaign but exerts significant influence in the anti-government campaign. Expressive language, though less dominant, helps sustain anti-government mobilization. Our findings extend Information Systems research by linking word-level linguistic functions to macro-level processes of digital organizing and sensemaking on social media. This socio-cognitive approach advances IS theory on information diffusion and collective action while providing methodological tools for analyzing platform-mediated mobilization. The results also hold practical value for IS scholars, policymakers, and platform designers seeking to understand how language shapes civic engagement and digital governance.
Recommended Citation
Amure, Ridwan and Agarwal, Nitin, "Modeling Word-Level Functions in Social Movement Discourse" (2025). ICIS 2025 Proceedings. 30.
https://aisel.aisnet.org/icis2025/user_behav/user_behav/30
Modeling Word-Level Functions in Social Movement Discourse
This study investigates how different types of language shape collective identity in digital protest movements. Using Instagram data from Brazil’s 2022 post-election unrest, we apply a computational framework that categorizes language into conative, referential, emotive, and expressive functions. Our analysis shows that referential and emotive language strongly predict the rise of collective identity. Conative language plays a transient role in the pro-government campaign but exerts significant influence in the anti-government campaign. Expressive language, though less dominant, helps sustain anti-government mobilization. Our findings extend Information Systems research by linking word-level linguistic functions to macro-level processes of digital organizing and sensemaking on social media. This socio-cognitive approach advances IS theory on information diffusion and collective action while providing methodological tools for analyzing platform-mediated mobilization. The results also hold practical value for IS scholars, policymakers, and platform designers seeking to understand how language shapes civic engagement and digital governance.
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