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Communications of the Association for Information Systems

Author ORCID Identifier

Ridwan Amure: https://orcid.org/0000-0001-5874-5871

Nitin Agarwal: https://orcid.org/0000-0002-5612-4753

Mainuddin Shaik: https://orcid.org/0000-0002-5506-3231

Abstract

This study addresses a key gap in digital mobilization research: how different types of language shape the emergence and evolution of collective action on social media. While prior work has explored sentiment and narratives, few studies have examined how functional language operates across time to influence mobilization dynamics. Using the Diffusion of Innovations (DOI) framework, we analyze four campaigns on Instagram and TikTok: Brazil’s anti- and pro-government protests (64,280 and 21,517 posts, respectively) and Taiwan’s 2024 disinformation and anti-disinformation efforts (23523 and 23471 posts, respectively). We categorize language into four functional types—conative, referential, emotive, and expressive—and track their influence on collective identity using temporal linguistic analysis and causality tests. Findings show that each campaign exhibits a distinct linguistic trajectory. For example, anti-government protests began with conative language to spark action, while pro-government content emphasized emotive unity. In Taiwan, anti-disinformation efforts used expressive language to build solidarity before shifting to referential and conative cues. At the same time, disinformation campaigns front-loaded referential claims to establish doubt, followed by emotive appeals to maintain engagement. This study contributes a novel computational framework for examining language-driven mobilization, offering theoretical and practical insights for researchers, platform designers, and policymakers concerned with digital activism, information integrity, and civic technology.

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