The theoretical foundation of the work lies in decades of social psychology research on collective action. According to these theories, people do not mobilize simply because they are angry. Coordinated behavior is mediated by a chain of interacting collective mental states: a perception of shared grievance, the emergence of corrective action norms held in common, and efficacy beliefs, the conviction that acting together can actually change the outcome. Crucially, these states are thought to interact and reinforce one another, with a shared social identity acting as the glue that binds individual discontent into group-level readiness to act. What has always been difficult is testing this cascade at scale, in the wild, as it unfolds in real time.

The researchers tackled that challenge by proposing an operationalization that translates each theoretical mental state into a measurable feature of online discourse. Grievance, in this framework, corresponds to talk about the alleged wrong, in this case the discussion of claimed voter fraud following the presidential elections. Validation corresponds to mutual affirmation among users, the conversational acts by which participants confirm one another’s perceptions. Shared identity corresponds to language that signals belonging to a like-minded community, and efficacy beliefs and action discourse correspond to expressions that coordinated effort is possible and calls to act. By mapping abstract constructs onto observable language, the team made it possible to track the ebb and flow of collective psychology across millions of messages.

The analytical toolkit was correspondingly rigorous. Rather than relying on simple correlations, the authors deployed trend analysis to characterize long-term movement in each discourse measure, stationarity tests to determine whether the underlying statistical properties of the conversation were stable over time, vector autoregression to model how each state predicted the others at subsequent points, and pathway analysis to trace the full sequence of influence from grievance through validation, identity, and efficacy to action talk. This combination allowed them to ask not merely whether the mental states co-occurred, but whether changes in one reliably preceded and predicted changes in another, which is the temporal signature the theories demand.

The dataset centered on communal discussion of alleged voter fraud surrounding the 2020 and 2024 U.S. presidential elections, a topic that dominated online political conversation in both cycles. The 2020 election and its aftermath, culminating in the events of January 6, 2021, provided a period in which fraud claims circulated intensely and mobilization discourse was highly visible. The 2024 election offered a natural comparison: similar claims, similar platforms, similar communities, but a different political and legal context. Comparing the two allowed the researchers to ask whether the psychological coupling observed in one context is a general feature of online collective action or a product of specific circumstances.

For the 2020 to 2021 period, the findings read like a textbook confirmation of the theory. Grievance about the alleged electoral fraud predicted subsequent mutual validation among participants. Validation, in turn, predicted the emergence of shared social identity. And shared identity predicted both efficacy beliefs and action discourse. In other words, the canonical interaction sequence that social psychologists have proposed, in which a common complaint is validated, hardens into a collective ‘we,’ and then fuels the belief that action is both possible and warranted, appeared in the data as a genuine temporal cascade. The results were consistent with a collective psychological alignment that strengthened progressively in the run-up to January 6, 2021.

The 2024 to 2025 results told a fundamentally different story. The analysis did not reliably support either the overall psychological alignment or the canonical sequence. Instead, the relationships among the five states were often negative or weak. Grievance could suppress efficacy rather than boost it, suggesting that in this context, dwelling on the alleged wrong did not translate into confidence that collective action would succeed. Action discourse could reduce grievance, an inversion of the expected direction of influence. Shared identity predicted validation, but it did not consistently predict action. The chain that had linked complaint to mobilization four years earlier had, in this electoral cycle, broken at multiple points.

The authors draw a sobering conclusion from this contrast: the coupling among grievance, validation, identity, efficacy, and action in digital conversation is context-dependent rather than universal. This matters for anyone who models online mobilization or designs interventions against it. A framework that assumes grievance reliably fuels action talk will fit some episodes well and others poorly, and the same community can behave in psychologically distinct ways across successive political moments. The study confirms specific theoretical mechanisms behind collective action in one electoral context while identifying the conditions under which the mobilizing alignment fragments in another, which is precisely the kind of boundary-drawing that turns a plausible theory into a tested one.

Methodologically, the paper also demonstrates how psychological theories can be operationalized in online discourse without losing their theoretical content. The mapping from mental states to discourse features, combined with time-series techniques such as vector autoregression and pathway analysis, gives researchers a template for studying collective psychology as a dynamic system rather than a static snapshot. The approach does not claim to read minds; it tracks the public linguistic traces of interacting mental states and tests whether their temporal ordering matches theoretical predictions. That distinction is important, and the authors are careful to frame their measures as discourse-based proxies whose interpretation depends on context.

The broader implications reach beyond electoral politics. Any movement, cause, or community that organizes online passes through some version of the grievance-to-action pipeline, and the finding that this pipeline can strengthen dramatically in one period and dissolve in the next suggests that researchers, platform designers, and policymakers should treat the psychological alignment of online communities as a variable to be measured, not an assumption to be made. With roughly 90 million posts underpinning the analysis, the study stands as one of the clearest demonstrations yet that the road from shared anger to collective action is real, measurable, and far from guaranteed.

Subject of Research: Quantitative analysis of collective action psychology in online election fraud discourse

Article Title: Online identity and action discourse around the 2020 and 2024 U.S. presidential elections

Article References: Lipatov, M., Illari, L., Sear, R., Verma, A., Johnson, N. F., & Gavrilets, S. (2026). Online identity and action discourse around the 2020 and 2024 U.S. presidential elections. PLOS Complex Systems, 3(5), e0000107. https://doi.org/10.1371/journal.pcsy.0000107

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000107

Keywords: collective action, social identity, social media, U.S. elections, voter fraud discourse, vector autoregression, grievance, efficacy beliefs, PLOS Complex Systems, January 6, online discourse, political psychology