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\title{Identity Without Action: 

Rethinking Collective Action Models in Disinformation Research}

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\author{Lorella Viola \\
  Vrije Universiteit Amsterdam / De Boelelaan 1105 \\
  1081 HV  \\
  Amsterdam, The Netherlands \\
  \texttt{l.viola@vu.nl} \\}

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\begin{document}
\maketitle
\begin{abstract}
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 Identity
Theory (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 reveal
that 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.\end{abstract}

\section{Introduction}
Online disinformation, understood here as the deliberate dissemination of false or misleading content with the potential to cause public harm \cite{tucker_social_2018}, has surged in recent years, particularly in the wake of the COVID-19 crisis. Widely acknowledged as a major threat to public and individual safety, substantial research has examined its structure \cite{van_prooijen_belief_2018,van_prooijen_conspiracy_2018}, spread \cite{bonnevie_quantifying_2021}, impact \cite{simms_impact_2020,stabile_sex_2019,chen_mislabeled_2020}, and content \cite{wiggins_nothing_2023,demata_conspiracy_2022,fallis_conceptual_2009}. Scholars have also investigated the role of conspiracy theories and disinformation in shaping public perceptions and decision-making \cite{chen_effects_2021,yagi_hpv_2024}, as well as the influence of network structures and user interactions in amplifying disinformation \cite{quintana_polarization_2022,gunaratne_temporal_2019}. More recently, attention has turned to understanding the deeper mechanisms through which disinformation persuades individuals to accept unlikely or false narratives, giving more importance to the cognitive and identity-based factors that may explain why individuals engage with disinformation \cite{reddi_identity_2023,bastick_would_2021,butter_routledge_2020}.

Despite the rapid growth of this field, however, the fundamental reasons behind user engagement with disinformation remain poorly understood. Some researchers argue that this gap stems from contradictory and fragmented findings \cite{birchall_conspiracy_2022,kirchner_countering_2020}, while others point to the overly functionalist approach to disinformation, treating it as merely `the opposite of true' and overlooking its cognitive and subjective dimensions \cite{viola_barren_2025,reddi_identity_2023,bastick_would_2021}. This often leads to disinformation consumers being dismissed as irrational or paranoid actors and to counter-measures being mostly inefficient \cite{alava_youth_2017,conway_determining_2017,johnson_self-radicalization_2018,molmen_mechanisms_2021,reicher_fueling_2016}. This scholarship also contends that engagement with disinformation is not merely an act of passive belief but an active discursive process, where individuals construct and negotiate their cultural identities. It further argues that interacting with disinformation, through shares, comments, reactions, and reposts, constitutes a discursive practice that equally encodes collective agency. This behaviour would be shaped by perceived injustice, disillusionment with mainstream media, and the affordances of alternative information ecosystems \cite{wintterlin_its_2023}. 

These arguments are supported by two key considerations. First, much of the current research on disinformation remains disproportionately focused on a small subset of industrialized democracies, particularly the United States and the United Kingdom \cite{bajaj_digital_2024}. A study by Seo \& Faris \cite{seo_comparative_2021} found that 62.8\% of empirical studies published in communication journals between 2015 and 2020 relied on U.S.-based data (p. 1166). Scholars such as Bajaj \cite{bajaj_digital_2024} highlight that disinformation is not a universal phenomenon and that this geographical bias distorts our understanding of its cultural dimensions. Consequently, mitigation efforts that ignore these cultural dynamics risk being ineffective.

Second, disinformation has been linked to citizens’ decrease of trust in mainstream media and other sources of authoritative information \cite{macfarquhar_powerful_2016,lewis_media_2017,allcott_social_2017}, mistrust of establishment political figures and institutions and increased acceptance of, or indeed support for, fringe, anti-establishment or radical actors and movements \cite{beauchamp_social_2019,amlinger_gekrankte_2022,reichardt_misstrauensgemeinschaft_2022}. Research on the 2020 health crisis, for example, has demonstrated that COVID-19 disinformation motivated individuals to protest by offering a sense of agency and empowerment \cite{reichardt_misstrauensgemeinschaft_2022,amlinger_gekrankte_2022,birchall_conspiracy_2022}. Thus, according to this view, even unlikely or improbable disinformation narratives succeed in mobilizing individuals by fostering a belief in their capacity to effect change. In this sense, online participation, e.g., expressing dissent, signing petitions, or sharing content, would function as a low-cost form of collective action (CA) \cite{brunsting_social_2002}. Through social media, users would be able to signal group membership, express opposition to elites, and reinforce a shared identity, transforming engagement with disinformation into a performative act of resistance. Online activities such as sharing or liking would in this way offer an easy and effective way for people to express dissent with others or to demonstrate their belonging to a group.

Building on this literature, the present study investigates engagement with disinformation on social media through the lens of social identity and CA, while explicitly questioning whether these frameworks adequately capture platform-mediated interaction. Rather than assuming that observable engagement reflects intentional mobilization or participatory efficacy, the study examines whether CA language functions as a universal engagement booster, or if its apparent effect is a byproduct of the higher baseline activity inherent to specific linguistic communities. To this end, we analyse a bilingual corpus of 4,085 English and Spanish posts from X associated with disinformation and conspiracy theory narratives. By combining BERTopic for narrative discovery with non-parametric statistical testing (Mann-Whitney U) and Random Forest Regressor to predict engagement levels, we assesses whether call-to-action CTA) vocabulary provides a consistent engagement effect (i.e., likes, reposts, quotes, replies) across groups, or if engagement is primarily driven by identity-based community behaviour. This distinction is central in refining how CA models are applied to social networks and reassessing the validity of engagement metrics as proxies for real-world mobilization connected to disinformation.

\section{User engagement, disinformation, and collective action}
The scholarly literature on user engagement in online and social media contexts has approached this phenomenon through various conceptual frameworks and methodologies. Engagement is often conceptualized as user-initiated actions that contribute to value co-creation, as proposed by Brodie et al. \cite{brodie_consumer_2013}. This broad definition underscores the interplay between behavioural, cognitive, and emotional dimensions of engagement, emphasizing the need to explore its motivations and nuances. Shao \cite{shao_understanding_2009} categorized user interaction into three primary behaviours: consumption (viewing and reading), participation (interacting with content), and production (creating and uploading content). Following this framework, researchers have examined how engagement manifests across platforms, particularly differentiating between active participation and passive consumption. On Facebook and YouTube, active engagement involves actions such as liking, commenting, and sharing, whereas passive engagement consists of clicking, watching, or hovering over content \cite{kaur_liking_2019,khan_social_2017}. On X, active engagement further includes reposting, quoting, and following \cite{chen_tweet_2011}. Studies also highlight the prevalence of passive users (often called `lurkers'), who primarily consume content without actively engaging, comprising up to 90\% of users in many online communities \cite{nonnecke_shedding_1999,preece_top_2004}. This contrast between active contributors and passive consumers underscores the need to understand what motivates users to actively engage with content, particularly disinformation.

Due to the urgency of the topic, recent research has therefore explored the drivers of engagement with disinformation and fake news on social media. Emotionally charged content has been identified as one of the strongest amplifiers of engagement, with sensationalized headlines and narratives strategically crafted to trigger emotional responses such as fear, anger, and anticipation, thus encouraging interaction and dissemination \cite{horner_emotions_2023}. Additionally, visual elements seem to play a significant role in enhancing credibility and audience response \cite{cao_exploring_2020,viola_what_2025}. Features such as clickbait, emotionally charged language, and references to specific individuals, organizations, or events further heighten emotional resonance, thereby boosting engagement \cite{ali_panic_2023}. Other factors influencing the likelihood of sharing disinformation would include fear of missing out, source credibility, information quality, cognitive overload, and social media fatigue \cite{kumar_amused_2020,islam_misinformation_2020}. These content strategies would capitalize on psychological triggers to grab attention, manipulate perceptions, and enhance virality. This study builds upon these findings and
integrates the Social Identity Theory (SIT) \cite{tajfel_social_2004} with the SIMCA model \cite{turner_social_1991,tajfel_social_2004,van_zomeren_toward_2008} to provide a theoretical lens for understanding engagement with disinformation. 

\section{Methodology}\label{methodology}
This study integrates SIT \cite{tajfel_social_2004} with the SIMCA model \cite{van_zomeren_toward_2008} to provide a theoretical lens for testing engagement with disinformation. Rather than accepting these models as given, we use them to formulate competing hypotheses regarding the drivers of user engagement. Engagement is operationalized as the cumulative sum of likes, shares, reposts, quotes, and replies associated with each post, as found in the literature\footnote{While high engagement metrics can indicate both support and heated opposition, from the SIMCA theoretical perspective, both reactions indicate that the content successfully triggered an engagement response, regardless of valence.} \cite{chen_tweet_2011}. 

SIT posits that individuals derive a significant portion of their self-concept from their perceived membership in social groups. In the context of this study, the ``in-group'' is operationalised as the linguistic community (i.e., Anglosphere vs. Hispanosphere). The author acknowledges that language is not a one-to-one substitute for cultural identity \citep{edwards_language_2009}. At the same time, however, in the context of transnational disinformation particularly on social media, language barriers often form the primary perimeter of information ecosystems. Evidence shows that language is not merely symbolic but also structural: it organizes who interacts with whom and, by extension, what information circulates within which audiences. With regard to Twitter specifically, Hale \citeyearpar{hale_global_2014} demonstrates that its connectivity is strongly stratified by language, with interaction patterns clustering within language communities and comparatively fewer ties crossing language boundaries; multilingual users can bridge communities, but they do not erase the underlying segmentation. Similarly, Eleta and Golbeck’ work on multilingual Twitter \citeyearpar{eleta_multilingual_2014} finds that most discourse remains anchored within language-specific public confirming that interaction on platforms frequently aligns with language-defined communities. 
Crucially, SIT suggests that different groups may develop distinct norms of interaction. Therefore, high engagement levels within a specific linguistic community may not necessarily reflect the quality of the content, but rather a community-specific baseline of expressive responding \cite{wintterlin_its_2023}. This framework helps explain why distinct engagement cultures might emerge independent of specific mobilizing cues, helping us to test the valence of SIT for disinformation research.

The SIMCA framework on the other hand posits that collective action is driven by three psychological predictors: social identity, perceived injustice, and participatory efficacy \cite{van_zomeren_toward_2008}.
Applied to disinformation, SIMCA implies that narratives framing mainstream institutions as corrupt (Injustice) or urging users to ``wake up'' (Efficacy) should trigger a transition from passive consumption to active distribution. If SIMCA is universally applicable to social media disinformation, the presence of CA language should act as an engagement booster regardless of the language spoken, due to its explicit signalling of injustice and efficacy. By contrast, if engagement is symbolic, interaction may be driven primarily by the user's cultural baseline, with mobilizing language having little to no additive effect. We test this hypothesis using non-parametric statistical testing (Mann-Whitney U). To further validate the relative impact of mobilizing language compared to identity and contextual factors, we trained a Random Forest Regressor to predict engagement levels.

To identify CTA expressions without relying on a priori assumptions, we adopted a two-step `Human-in-the-Loop' approach. We first applied Embedding-Based Topic Modelling using BERTopic \cite{grootendorst_bertopic_2022} to identify latent narrative clusters, the most driving topics, and a post excerpt representative of each topic. For each topic, we then qualitatively inspected the top 10 representative terms associated with mobilization. From these terms, we extracted a lexicon of explicit CTA expressions such as `Resist the reset', `donotcomply'), power-laden language (e.g., us vs. them frames and agency attribution such as `Say no to digital IDs', `Rise up!') and thematic organisation (e.g., New World Order, WEF Puppets). A post was coded as having CTA expressions only if it contained language from this manually validated lexicon. This ensured that the classification was interpretable and not subject to the noise of probabilistic topic modelling. Additionally, the author applied Critical Discourse Analysis (CDA) \cite{dijk_discourse_1985,dijk_discourse_1997} on a randomised sample of 200 posts (100 posts per language) to extract further expressions. The list of CTA expressions is provided in Table \ref{tab:cta_expressions}. A visual representation of the workflow is provided in Figure \ref{fig:workflow_overview}.

\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{Workflow_overview.png}
  \caption{Research design: methodology overview.}
  \label{fig:workflow_overview}
\end{figure}

\section{Data-set} \label{data}
Data retrieval was conducted through targeted queries that extracted posts containing
specific hashtags, including \emph{\#Agenda2030}, \emph{\#The GreatReset}, \emph{\#NewWorldOrder}, \emph{\#wefpuppets}. These hashtags have been found in the literature as typically associated with disinformation, fake news, and conspiracy theories \cite{laquieze_lgbtpropaganda_2025,christensen_great_2023,saad_abdullahi_tracking_2023}. The full list of the seed hashtags is provided in the \hyperref[sec:appendix]{Appendix}. Additionally, the data-set was enriched with information about content diffusion and filtered for posts drawn from sources known for low credibility, as identified in the Iffy+ list \cite{golding_iffy_2025}, which catalogues 2,042 outlets flagged by professional fact-checkers as non-reliable sources, a method commonly employed in prior work \cite{deverna_identifying_2024,yang_covid-19_2021}\footnote{The author acknowledges that while this strategy allows for broad coverage, it cannot account for the fact that not all the posts in the data-set contain links to external content and that individual articles from a low-credibility outlet may still be accurate.}.

The data-set was later pseudonymised to remove any identifiable references and it can be provided upon request to the author. 
The working data-set covers 394 days from 1 August 2022 to 30 August 2023. It contains 4,035 posts in two languages almost identically distributed (2,000 in English - EN, 2,035 in Spanish - ES) thus making the two sets highly comparable. The data-set also includes several attributes such as the post texts, the hashtags, likes, replies, reposts, shares, and quotes count. A detailed description is given in Table~\ref{tab:data-set-description} in the \hyperref[sec:appendix]{Appendix}.

\section{Analysis}
\subsection{Superspreaders and bot activity}
First we remove superspreaders and bot activity from the dataset. Superspreaders were identified using a multi‐dimensional engagement and network‐activity criterion, operationalized according to three criteria: per‐user engagement metrics, conversational out‐degree, and thresholding \cite{deverna_identifying_2024}. Per-user engagement was calculated by computing three summary statistics on repost volume and three on reply volume for each user in the data-set as explained below:

\begin{itemize}[noitemsep, topsep=0pt]
  \item \textbf{Repost sum} (\(\mathrm{rt\_sum}_i\)): the total number of times user \(i\)’s posts were reposted.
  \item \textbf{Repost mean} (\(\mathrm{rt\_mean}_i\)): the average number of reposts per post for user \(i\).
  \item \textbf{Repost max} (\(\mathrm{rt\_max}_i\)): the highest repost count received by any single post of user \(i\).
  \item \textbf{Reply sum} (\(\mathrm{rp\_sum}_i\)): the total number of replies received by user \(i\)’s posts.
  \item \textbf{Reply mean} (\(\mathrm{rp\_mean}_i\)): the average number of replies per post for user \(i\).
  \item \textbf{Reply max} (\(\mathrm{rp\_max}_i\)): the maximum number of replies received by any single post of user \(i\).
\end{itemize}

These six metrics captured both total volume (sum), typical activity level (mean), and individual peaks (max). Conversational out-degree was computed to assess how broadly each user initiated or contributed to discussions. This was operationalized by constructing a directed reply network in which each edge represented a reply from one user to another. A user’s out-degree thus indicated the number of distinct users they replied to. Finally, the top five percent of users on each metric were identified independently to define the superspreader threshold.
A user was designated a superspreader if they ranked in the top 5 percentile on any of the five measures (aggregate reposts, peak reposts, aggregate replies, peak replies, or out‐degree). This approach ensured that both consistently active amplifiers and users responsible for individual cascades could be captured. The results stratified per language are displayed in Table \ref{tab:superspreader_summary} below.

\begin{table*}[h]
  \centering
  \renewcommand{\arraystretch}{1.05} % Increase row height
  \small
  \begin{tabular}{|c|c|r|r|r|r|r|}
    \hline
    \textbf{Language}
      & \textbf{Statistic}
      & \textbf{out-degree}
      & \textbf{rp-max}
      & \textbf{rp-sum}
      & \textbf{rt-max}
      & \textbf{rt-sum} \\
    \hline
    \multirow{7}{*}{EN}
      & 50\%   &  3.00 &  1.00 &  2.00 &   2.00 &   3.00  \\
      & 75\%   &  5.00 &  2.00 &  3.00 &  10.00 &  15.25  \\
      & Total  &248.00 &248.00 &248.00 & 248.00 & 248.00  \\
      & Max    & 45.00 &131.00 &219.00 & 808.00 &2495.00  \\
      & Mean   &  4.25 &  4.17 &  6.73 &  20.61 &  44.07  \\
      & Min    &  0.00 &  0.00 &  0.00 &   0.00 &   0.00  \\
      & Std    &  7.00 & 13.16 & 22.98 &  80.74 & 216.37  \\
    \hline
    \multirow{7}{*}{ES}
      & 50\%   &  1.00 &  1.00 &  2.00 &   2.50 &   4.00  \\
      & 75\%   &  4.00 &  2.00 &  3.00 &  10.00 &  13.75  \\
      & Total  &286.00 &286.00 &286.00 & 286.00 & 286.00  \\
      & Max    & 48.00 &354.00 &440.00 & 808.00 &2495.00  \\
      & Mean   &  3.20 &  5.50 &  7.57 &  19.75 &  35.78  \\
      & Min    &  0.00 &  0.00 &  0.00 &   0.00 &   0.00  \\
      & Std    &  5.42 & 25.39 & 33.42 &  75.23 & 191.84  \\
    \hline
  \end{tabular}
  \caption{Summary statistics (50th \%, 75th \%, total count, maximum, mean, minimum, and standard deviation) of superspreader metrics by language (EN vs.\ ES).}
  \label{tab:superspreader_summary}
\end{table*}

Most notable insights include that among the identified superspreaders, English users (N = 248) tend to reply to more distinct users (median out-degree = 3) than Spanish users (median = 1). Spanish superspreaders receive slightly more replies on average (mean rp-sum = 7.57 vs. 6.73) and exhibit higher reply peaks (mean rp-x = 5.50 vs. 4.17). Repost behaviour is comparable across languages: mean repost peak (20) and sum (44 EN vs. 36 ES) show only modest differences. Based on the metrics, a total number of 1,915 superspreaders was identified (940 for English and 975 for Spanish). This means that in the English sub-set, superspreaders account for about 43\% of posts, whereas in the Spanish sub-set, superspreaders contribute around 48\%. 

The remaining accounts were further inspected for potential bot activity using \emph{BotometerLite} \cite{observatory_on_social_media_botometer_nodate}. \emph{BotometerLite} produces scores derived from historical Twitter data collected prior to 31 May 2023, thereby aligning with the data-set’s temporal scope and estimating the likelihood that a user was a bot at the time of observation. As none of the accounts was scored as a potential bot, no further filtering was applied to the data-set which finally included 1,140 posts in English and 1,060 in Spanish, making the two sets once again highly comparable. 

\subsection{Statistical analysis of language and engagement}
The analysis now examines possible engagement differences between the two communities that can indicate that audience responses vary significantly according to community identity factors. To address the non-normal distribution of engagement metrics in social media data (Shapiro-Wilk $p < .001$), we employed the non-parametric Mann-Whitney U test. The results indicated a statistically significant difference in engagement between the language groups ($U = 558123.0, p < .001$). Although the median engagement for both groups was 0 (reflecting the long-tail nature of the data after removing superspreaders), the Spanish subset exhibited a higher mean engagement ($M = 1.87$) compared to the English subset ($M = 1.37$). The effect size was small ($r = 0.076$), suggesting that while language plays a statistically significant role in engagement patterns, it is likely one of multiple contributing factors. To ensure robustness, we repeated this analysis on the full dataset including superspreaders (Total N=4,035). The difference remained statistically significant ($U = 1,869,946, p < .001, r = 0.081$), confirming that the observed cross-linguistic difference is not an artifact of outlier exclusion. The results of both tests are displayed in Table \ref{tab:mann_whitney_results}. 
In the next section, the analysis tests the effect of CTA expressions in driving engagement with disinformation for both groups. 

\begin{table}[t]
\centering
\footnotesize
\setlength{\tabcolsep}{2pt}
\begin{tabular}{@{}l r r c c c c@{}}
\toprule
\textbf{Group} & \textbf{N} & \textbf{Mean} & \textbf{Mdn} & \textbf{$U$-Stat} & \textbf{$p$} & \textbf{$r$} \\
\midrule
\multicolumn{7}{l}{\textbf{Dataset: Filtered} (No Superspreaders)} \\
EN & 1,140 & 1.37 & 0.0 & \multirow{2}{*}{558,123} & \multirow{2}{*}{$<.001$} & \multirow{2}{*}{.08} \\
ES & 1,060 & 1.87 & 0.0 & & & \\
\midrule
\multicolumn{7}{l}{\textbf{Dataset: Full} (With Superspreaders)} \\
EN & 2,000 & 8.72 & 1.0 & \multirow{2}{*}{1,869,946} & \multirow{2}{*}{$<.001$} & \multirow{2}{*}{.08} \\
ES & 2,035 & 14.19 & 1.0 & & & \\
\bottomrule
\end{tabular}
\caption{Comparison of engagement metrics between English (EN) and Spanish (ES) groups with and without superspreaders}
\label{tab:mann_whitney_results}
\end{table}

\subsection{Collective action} \label{collective action}
This part of the analysis now investigates the role of CA language in user engagement with disinformation posts. The tested hypothesis is that perceived injustice strengthens motivation for collective action, which in an online environment would translate into engaging with and sharing disinformation as an act of defiance and regained agency. First, we identified CTA expressions combining the statistical (topic modelling) and linguistic (CDA) methodology as explained in \ref{methodology}. 
\subsubsection{Topic modelling}
Topic modelling was used to identify posts containing mobilising language and call-to-action expressions. Specifically, we used BERTopic with a multilingual SentenceTransformer \cite{grootendorst_bertopic_2022} as in the literature, it is found to outperform traditional LDA models on multilingual, short texts. Feature extraction was performed by applying a bag-of-words with unigrams and bigrams, and  \(\texttt{max\_df}=0.85\) and \(\texttt{min\_df}=0.02\) to remove overly common/rare terms. Preliminary runs with unconstrained topic discovery produced a large number of fine-grained clusters, many of which overlapped semantically. Setting \texttt{nr\_topics=10} allowed for a more coherent, high-level representation of discourse themes while retaining sufficient diversity to capture major narrative patterns across languages. This choice also ensured comparability across analyses and facilitated qualitative interpretation. The model returned per-document topic probabilities, per-topic term rankings, and most representative document per topic, enabling both quantitative summaries and qualitative interpretation across languages crucial for the extraction of CTA expressions.

Several topics identified by the model displayed strong mobilising language, encouraging resistance, participation, or direct action against perceived threats linked to Agenda2030 and related governance narratives. Representative posts (Table~\ref{tab:bertopic}) show imperatives such as `wake up', `join' and `resist', reflecting how conspiracy-linked discourses often blend moral urgency with collective calls to action. These patterns suggest that in disinformation discourse, mobilising language is often present as shared narratives of opposition and control.

\begin{table*}[ht]
\centering
\scriptsize
\begin{tabular}{|c|c|p{5cm}|p{7cm}|}
\hline
\textbf{Topic ID} & \textbf{Count} & \textbf{Top Representative Terms} & \textbf{Representative Example (excerpt)} \\
\hline
0 & 1689 & agenda2030, nwo, video, com, world, order, people, global, reset, truth & “Immortalized cell lines used in lab-grown meat... global elites want to control the food supply.” \\
\hline
1 & 362 & chile, españa, onu, méxico, argentina, política, gobierno, países, sociedad, latinoamérica & “Y ahora @GiorgioJackson, @gabrielboric... hablan de la ONU y la Agenda2030 en Latinoamérica.” \\
\hline
2 & 292 & climatescam, climate, agenda2030, globalwarming, wef, co2, fake, scam, science, propaganda & “Desmintiendo el calentamiento global bufones de la ONU... \#ClimateScam.” \\
\hline
3 & 46 & canada, canadians, trudeau, agenda2030, climate, policy, protest, covid, freedom, rights & “Canadians need to support Alberta Oil \& Gas — Trudeau is destroying our economy for Agenda2030.” \\
\hline
4 & 37 & vaccine, covid, depopulation, populationcontrol, health, agenda, wef, control, elite, world & “Vaccines are part of the depopulation agenda — open your eyes.” \\
\hline
5 & 26 & climatechange, greenagenda, sdgs, un, sustainability, policy, leaders, summit, goals, development & “The UN Climate Summit pushes Agenda2030 — same green agenda with new branding.” \\
\hline
6 & 19 & esg, economy, finance, corporations, capitalism, elites, governance, power, wef, global & “ESG is just a corporate version of Agenda2030 — economic control by the few.” \\
\hline
7 & 16 & energy, oil, gas, renewables, canada, europe, policy, crisis, cost, transition & “Energy transition in Europe is a scam — higher costs and less freedom.” \\
\hline
8 & 14 & digitalid, cbdc, surveillance, government, privacy, citizens, control, technology, freedom, rights & “Digital ID and CBDCs mean full surveillance — say no to control systems.” \\
\hline
9 & 12 & conspiracy, hoax, lies, fake, propaganda, media, agenda2030, truth, narrative, misinformation & “The media lies again — Agenda2030 is propaganda to hide the real plan.” \\
\hline
\end{tabular}
\caption{Topics identified by BERTopic with document count, most representative keywords, and a representative post excerpt.}
\label{tab:bertopic}
\end{table*}

Additionally, further expressions were extracted applying CDA \cite{dijk_discourse_1985,dijk_discourse_1997} on a sample of 200 posts (100 posts per language). The full list of expressions (91 in English and 82 in Spanish) is provided in Table \ref{tab:cta_expressions}. 

\begin{table}[ht]
  \centering
  \small
  \begin{tabular}{@{}p{0.45\linewidth}@{\quad}p{0.45\linewidth}@{}}
    \toprule
    \textbf{EN} & \textbf{ES} \\
    \midrule
    wake, resist, join, sign, take action, act, must, do not comply, resistthereset, stop, share, help, demand, make, stand, stand up, speak out, raise your voice, get involved, take a stand, participate, march, protest, mobilize, defend, spread the word, volunteer, organize, boycott, take part, engage, rally, fight back, join the movement, act now, donotcomply
    &
    despierta, resiste, únete, firma la petición, actúa ahora, comparte, movilízate, haz algo, noalaagenda2030, noalplan, detente, toma, actúa, firma, ayuda, exige, haz, mantente, levántate, alza la voz, participa, actúa ya, protesta, lucha, defiende, propaga el mensaje, voluntario, organiza, boicotea, toma parte, únete al movimiento, comprométete, alístate, actúa hoy, únete ahora, haz tu voz escuchada, súmate, movilízate hoy
    \\
    \bottomrule
  \end{tabular}
  \caption{Call‐to‐Action Expressions by Language}
  \label{tab:cta_expressions}
\end{table}

\section{Call-to-Action and user engagement}
To test the hypothesis that mobilizing language functions as an engagement booster (as predicted by SIMCA), we now compared the engagement levels of posts containing CTA expressions against those without, within each language community. Results are displayed in Table \ref{tab:cta_booster_test}. Contrary to theoretical expectations, no significant engagement premium was found for mobilizing content in either group. In the English data-set, posts with CTA expressions did not elicit significantly higher engagement than non-CTA posts ($U = 57,446, p = .39$). Similarly, in the Spanish data-set the presence of mobilizing language had no significant impact on engagement levels ($U = 23,054, p = .56$). These findings suggest that in social media disinformation ecosystems, the mere presence of explicit `calls to fight' or `wake up' does not trigger increased user interaction, reinforcing the interpretation that engagement is driven by community norms, e.g., identity rather than mobilizing appeals, i.e., action.

\begin{table}[t]
\centering
\footnotesize % Reduces font size to fit column
\setlength{\tabcolsep}{2.5pt} % Reduces space between columns
\begin{tabular}{@{}ll r c c c c@{}}
\toprule
\textbf{Lang} & \textbf{CTA} & \textbf{N} & \textbf{Mdn} & \textbf{$U$} & \textbf{$p$} & \textbf{$r$} \\
\midrule
\multirow{2}{*}{\textbf{EN}} & Yes & 110 & 0.0 & \multirow{2}{*}{57,446} & \multirow{2}{*}{.39} & \multirow{2}{*}{-.01} \\
& No & 1030 & 0.0 & & & \\
\midrule
\multirow{2}{*}{\textbf{ES}} & Yes & 46 & 0.0 & \multirow{2}{*}{23,054} & \multirow{2}{*}{.56} & \multirow{2}{*}{.01} \\
& No & 1014 & 1.0 & & & \\
\bottomrule
\end{tabular}
\caption{Comparisons of engagement for posts with and without CTA expressions.}
\label{tab:cta_booster_test}
\end{table}

\section{Multivariate Analysis: Drivers of Engagement}
To further investigate the drivers of engagement, we trained a Random Forest Regressor using Language (Identity), Dominant Topic (Context), and Collective Action (Mobilization) as features.The model yielded a negative $R^2$ score ($-0.003$), indicating that textual and linguistic features alone cannot predict the magnitude of user engagement. This null result is however theoretically significant: it demonstrates that engagement in disinformation ecosystems is not a mechanical response to specific trigger words or topics, but likely a stochastic process driven by algorithmic amplification and network dynamics \cite{gonzalez-bailon_dynamics_2011}. At the same time, the Feature Importance analysis (Table \ref{tab:feature_importance} and Figure \ref{fig:feature_importance}) provides insight into the relative weight of these weak signals. Dominant Topic ($43.8\%$) and Language ($37.4\%$) largely outperformed CTA expressions ($18.8\%$). This ranking confirms that even among the weak textual predictors available, the context (Topic) and community (Language) carry twice as much weight as the mobilizing rhetoric (CTA).

\begin{table}[t]
\centering
\small
\begin{tabular}{@{}lc r@{}}
\toprule
\textbf{Feature} & \textbf{Category} & \textbf{Importance} \\
\midrule
Dominant Topic & Context & \textbf{43.8\%} \\
Language (EN/ES) & Identity & \textbf{37.4\%} \\
Collective Action & Mobilization & 18.8\% \\
\bottomrule
\end{tabular}
\caption{Feature Importance scores from the Random Forest Regressor predicting user engagement.}
\label{tab:feature_importance}
\end{table}

\begin{figure}[t]
    \centering
    \includegraphics[width=0.95\columnwidth]{feature_importance.png}
    \caption{Feature importance ranking from the Random Forest model predicting user engagement.}
    \label{fig:feature_importance}
\end{figure}

\section{Discussion}
This study provided empirical evidence that challenges the universalist application of the SIMCA model to disinformation on social media. By disentangling the effects of community identity from mobilizing language (CTA expressions), our analysis revealed that language and cultural identity remain a primary driver of engagement. Cross-linguistic comparison between the two language communities showed a significantly higher baseline of activity in the Spanish-speaking community compared to the Anglosphere, a difference that persisted even after excluding superspreaders. This finding supports the Social Identity Theory perspective that online communities exhibit distinct engagement behaviours heavily structured by language \citep{hale_global_2014}.

Second, and most critically, our results demonstrate that mobilizing rhetoric fails to translate into measurable action such as explicit endorsement. Contrary to the predictions of the SIMCA model, the presence of explicit CTA language provided no significant engagement effect in either language group. This null result was further corroborated by multivariate analysis, where the CA feature proved to be the weakest predictor of engagement (18.8\%), far outweighed by narrative context (43.8\%) and language identity (37.4\%). 

These findings fundamentally reframe how we should interpret engagement metrics in disinformation research. The fact that users do not engage more with posts demanding them to act, resist, or wake up suggests that their interaction is symbolic rather than functional. In other words, users likely engage explicitly with a disinformation post to signal their alignment with the group and opposition to the mainstream (identity performance), rather than exclusively signalling collective resistance.

These results encourage us to rethink current applications of collective action models to social media as they may overestimate the mobilizing nature of engagement. Digital interactions in this context should be viewed as low-cost expressive behaviours, that is forms of identity signalling that satisfy affective needs for belonging without necessarily implying a commitment to collective mobilization. Finally, by documenting the significant baseline disparity between Spanish and English communities, this study challenges the field’s reliance on Anglocentric data. It demonstrates that engagement is a culturally situated behaviour, proving that theoretical models built solely on English datasets cannot be generalized to other linguistic ecosystems without local validation.

\section{Conclusion}
This study set out to test the validity of the Social Identity Model of Collective Action (SIMCA) to social media disinformation. The findings suggest that engagement with disinformation functions primarily as symbolic identity alignment rather than functional collective action. Although mobilizing language is pervasive in disinformation discourse, its presence does not trigger the behavioural response predicted by collective action models. Instead, users appear to engage with content to signal group belonging and opposition to the mainstream, regardless of whether the content explicitly demands action.

By refining the application of SIT and SIMCA to social media, this study highlights the need to reconsider how engagement metrics are interpreted in disinformation research. While collective action models remain valuable for understanding how identities and grievances form, their explanatory power diminishes when mapped directly onto platform metrics. We argue that high engagement levels should be interpreted as signals of community cohesion and future research should therefore explore how cultural identity, emotional valence, narrative framing, and platform affordances shape identity-driven engagement with disinformation to develop more effective analytical frameworks and intervention strategies against the spread of disinformation online.

\section{Limitations}
While this study provides valuable insights, several limitations must be acknowledged. The data-set consists of posts from X, which may not be representative of broader social media engagement patterns on Facebook, YouTube, or TikTok. Future studies could investigate how different platforms' algorithmic amplification patterns alter engagement dynamics. 
Due to lack of resources, the study primarily analysed explicit collective action expressions from a sample of the data-set but did not account for \emph{all} the mobilization strategies in the data-set. Further research should incorporate larger semantic and qualitative analysis to assess a larger range of such linguistic strategies.

\section*{Declaration on Generative AI}
During the preparation of this work, the author used Gemini 3 in order to: Grammar and spelling check. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the publication’s content.

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\appendix
\section{Appendix}
\label{sec:appendix}

\begin{table}[ht]
  \centering
  \small
  \caption{data-set description}
  \label{tab:data-set-description}
  \begin{tabular}{lr}
    \toprule
        \textbf{Value} & \textbf{Count} \\
        \midrule
            Likes         & 26,007           \\
            Quotes         & 1,281            \\
            Replies         & 3,329   \\
            Reposts    & 15,693 \\
            Users & 2,091 \\
            Mentions & 1448 \\
            Hashtags &	13,691 \\            
        \bottomrule
    \end{tabular}
    \end{table}

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\section{Appendix: Seed Hashtags}
\label{sec:appendix_hashtags}

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\begin{table}[H] 
\centering
\footnotesize
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\caption{Frequency of seed hashtags in the dataset. The list is sorted by frequency.}
\label{tab:hashtag_counts}

\begin{tabular}{@{}lr | lr | lr@{}}
\toprule
\textbf{Hashtag} & \textbf{Count} & \textbf{Hashtag} & \textbf{Count} & \textbf{Hashtag} & \textbf{Count} \\
\midrule
\#Agenda2030 & 2,950 & \#chemtrails & 44 & \#klausschwab & 23 \\
\#agenda2030 & 336 & \#BillGates & 42 & \#WEFpuppet & 22 \\
\#WEF & 291 & \#SocialCreditSystem & 42 & \#noalaagenda2030 & 22 \\
\#ClimateScam & 147 & \#PureBlood & 40 & \#malditaagenda2030 & 22 \\
\#GreatReset & 144 & \#WEFpuppets & 38 & \#Fauci & 22 \\
\#NWO & 121 & \#billgates & 34 & \#depopulation & 21 \\
\#NewWorldOrder & 112 & \#Globalist & 32 & \#NetZero & 21 \\
\#Agenda2030. & 90 & \#ClimateCult & 32 & \#DictaduraSanitaria & 20 \\
\#AGENDA2030 & 82 & \#SDGs & 30 & \#Soros & 20 \\
\#NOM & 76 & \#TheGreatReset & 29 & \#CrimesAgainstHumanity & 20 \\
\#nwo & 68 & \#Bilderberg & 28 & \#Agenda2030? & 20 \\
\#Agenda2030, & 66 & \#Repentinitis & 28 & \#CBDCs & 19 \\
\#Agenda21 & 66 & \#NuevoOrdenMundial & 27 & \#vaccineinjuries & 18 \\
\#Plandemia & 65 & \#VaccineGenocide & 27 & \#CIA & 18 \\
\#DigitalID & 61 & \#FBI & 26 & \#agenda21 & 18 \\
\#ODS & 58 & \#wef & 26 & \#FueraONU & 17 \\
\#KlausSchwab & 57 & \#GeorgeSoros & 26 & \#WorldEconomicForum & 17 \\
\#DiedSuddenly & 56 & \#vaccine & 25 & \#Nuremberg2 & 16 \\
\#15minutecities & 55 & \#plandemia & 25 & \#Chemtrails & 16 \\
\#CBDC & 50 & \#ESG & 25 & \#15MinuteCities & 15 \\
\#DepopulationAgenda & 50 & \#HunterBiden & 24 & \#Jarnac & 15 \\
\#climatescam & 48 & \#WEF2030Agenda & 24 & \#VaccineSideEffects & 14 \\
\#cbdc & 45 & \#repentinitis & 23 & \#CambioClimatico & 14 \\
\bottomrule
\end{tabular}
\end{table}

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