Despite the rapid growth of disinformation research, the fundamental reasons behind user engagement with such content remain poorly understood. Recently, several scholars have suggested that researchers should study engagement with disinformation as a form of collective action (CA). Drawing on Social IdentityTheory (SIT) and the Social Identity Model of Collective Action (SIMCA), this study empirically verifies this assumption by testing it across two distinct linguistic communities, English and Spanish. Specifically, it investigates whether mobilizing CA language functions as a uniform predictor of engagement, or if engagement is primarily driven by community specific identity dynamics. The experiment analysed a bilingual corpus of 4,035 X (formerly Twitter) posts associated with conspiracy theory and disinformation-related hashtags (e.g., #Agenda2030, #TheGreatReset). Using a mixed-methods approach combining BERTopic for narrative discovery, non-parametric statistical testing and Random Forest Regressor, we disentangled the effects of language presence from community behaviour. The results revealthat the Spanish community exhibits a higher baseline engagement compared to the English community indicating that engagement is primarily driven by macro-level community norms (i.e., identity) rather than micro-level linguistic triggers. We argue that rather than treating mobilizing language as a uniform predictor of engagement, future application of SIMCA in disinformation research should account for these identity-based baseline differences.

Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on conservative network constructions that erase initially sparse but salient signals. This paper proposes a novel multi-layer framework that captures low-frequency signals of emerging information disorder allowing for locating where online discourse is reframed and amplified over time. The use case is 14 years of Italian discourse on X regarding the Human Papillomavirus (HPV) vaccine across three pivotal epochs (2010–2024). Utilizing hashtag co-occurrence networks, we introduce a dual-layer approach. We first identify robust core discourse coalitions through conservative community detection, revealing a stable prevention-oriented backbone contrasted with increasingly separable skepticism coalitions. We then introduce a ‘coverage’ layer and project fringe hashtags into core coalitions based on weighted connectivity. Using a manually labelled set of skeptical and conspiratorial seed tweets, we demonstrate that this core–coverage projection significantly improves the recovery of long-tail, problematic hashtags while preserving an interpretable coalition structure. Our findings characterize the structural maturation of polarized narratives and provide a methodology for mapping how discourse is reframed and amplified by information disorder over time.