Modular Polyvocality: Community Detection and Semantic Analysis of Civil Society Discourse in English-Language #MahsaAmini Twitter Networks

Abstract

This study investigates how English-language Twitter discourse surrounding the #MahsaAmini protests (October 2022–February 2023) organized into distinct yet interconnected thematic communities within civil society networks. Combining Louvain community detection, sentiment profiling (via VADER), and semantic validation (Jensen–Shannon Divergence, MANOVA, cosine similarity), we map five protest “streams” reflecting modular issue alignments, from Documentation of events involving violence and On-the-Ground Mobilization to Monarchist Advocacy & Diaspora Political Engagement and Anti-IRGC Campaigns. While these communities differ in topical focus, their shared affective tone and overlapping lexicons point to modular polyvocality, a discourse structure characterized by thematic differentiation within broader rhetorical and affective coherence. This pattern represents polyvocal consensus rather than fragmentation. We interpret these findings through the lens of connective action, networked publics, and digital diaspora activism, arguing that the discourse reflects a transnational, modular, and affectively aligned subset of protest-oriented civil society mobilization within constrained information environments. This work contributes to debates on hybrid dissent, algorithmic visibility, and the structure of protest discourse in controlled digital contexts.