The need for such a framework arises because digital consultations can generate thousands of proposals, comments and arguments faster than researchers can read them. Computational tools already perform tasks such as topic detection, sentiment classification and proposal identification. Those tools answer what was discussed and how it was expressed, but they may miss politically consequential differences hidden within similar vocabulary. A citizen who writes that the state should consider a community’s perspective and one who demands that the state fulfil a commitment may attract the same topic and sentiment labels while occupying markedly different positions toward institutional authority.

Content analysis remains the first level of the proposed framework and the recommended initial pass. Drawing on computational psycholinguistics, it examines content-word frequencies, themes, named actors and expressed emotions as indicators of what participants attend to and how those patterns covary with psychological states. Such measures are inexpensive, scalable and comparable across large corpora, and they can summarize the concerns most prominent within a process. The authors treat them as necessary but insufficient: frequency counts can show that fairness, health or education dominates a consultation, yet they do not establish how citizens relate to the institutions receiving their contributions.

Generative language models make that limitation more consequential. Fluent systems can reproduce the surface vocabulary of citizen contributions, and human judges have sometimes struggled to distinguish machine-generated from human text under controlled conditions. The article does not establish that dictionary-based measures necessarily fail on participatory corpora; that is a transferred hypothesis requiring direct testing. Nevertheless, the possibility undermines any assumption that the mere presence of characteristic words proves authentic participation. The authors also warn that deeper analytical layers should not be treated as automatic authenticity detectors, because a model with access to interaction history and instruction can potentially reproduce synchrony or stable narrative positioning as well.

The second level examines synchrony between participants, especially convergence in their use of function words such as articles, prepositions, pronouns and other relatively automatic elements of speech. Language Style Matching quantifies this convergence, while Conversational Semantic Role Labeling can reconstruct semantic relationships whose arguments extend across conversational turns. The underlying evidence comes largely from English-language dyads, small groups and interactions without institutional consequences. Applying it to Spanish-language deliberation therefore requires transfer validation rather than assumption, particularly because Spanish marks grammatical person in verb endings, permits null subjects and uses clitics in ways that complicate direct transplantation of English categories.

Most importantly, the framework treats synchrony asymmetrically. Low synchrony between citizens and institutional respondents can count as evidence against communicatively oriented interaction because it suggests that the institution responded in its own categories without registering the citizen’s contribution. High synchrony cannot establish the opposite conclusion. Matching styles may reflect mutual understanding, but they can also arise through conflict, strategic accommodation, impression management, institutional scripting or automatic mimicry. The measure can therefore help identify deliberative failure without certifying deliberative success. Reports should state which observed pattern would count against their conclusion rather than converting a high value into a general quality score.

Several statistical safeguards are required before synchrony is interpreted. Topic must be held constant or modeled because legal, budgetary and territorial discussions distribute function words differently. Short contributions can yield unstable proportions, so researchers should define a minimum token threshold, match compared texts by length and report sensitivity analyses. Group-level values also require a level-specific null model: participants are repeatedly reassigned to pseudo-groups, the statistic is recomputed and observed synchrony is expressed as a deviation from that distribution. This prevents the arithmetic tendency of aggregated profiles to resemble one another from being misread as deliberative integration. Comparisons can then distinguish synchronization among individual citizens, between deliberative groups, across socially defined segments and between citizens and institutions.

Cross-level patterns supply the framework’s main diagnostic power. Citizens may synchronize strongly within groups while their groups fail to converge, indicating local cohesion without integration. Synchrony between demographic segments may begin at a structural baseline and decline among individuals, suggesting that deliberation attenuated pre-existing differences. Individual exchanges may be highly coordinated while citizen–institution synchrony remains low, indicating that discussion among citizens did not reach the convening body. The article proposes that, under favorable conditions, synchrony among individual citizens will exceed synchrony among groups once both are expressed relative to their respective null distributions. It presents this ordering as a hypothesis that could be reversed by evidence about process design, group composition or institutional relations.

The third level, narrative positioning, uses semantic role labeling to identify who acts, who undergoes an action, where it is directed and how power is encoded in the governing verb. Roles are aggregated for each actor across a corpus and projected onto an explicit structural description, allowing researchers to characterize a participant’s trajectory rather than reduce the person to one average power score. Possible projections include power-and-agency connotation frames, force-dynamic schemas and positioning theory. Every projection would require a transparent annotation scheme, independent coders, an agreement statistic and a procedure for resolving disagreements. Existing Spanish resources such as AnCora and AnCora-Co offer a starting point, but their predominantly newspaper-based text does not establish performance on colloquial deliberation.

The framework carefully separates four concepts that are often conflated: grammatical agency in a sentence, narrated agency across a corpus, subjective political efficacy as a psychological belief, and objective institutional influence as documented incorporation into decisions. It directly measures only the first two. A patient-dominant profile may accurately describe neglect rather than a participant’s deficiency, while people with formal power may construct sentences that suppress agency. The proposed dissociation is that participatory systems granting voice without transferring decision power may show rising narrated agency while actual influence remains flat. Testing that hypothesis would require co-measured efficacy scales and documentary evidence of institutional uptake, neither of which the article presents.

Implementation proceeds through four stages: segmenting a corpus into narrative episodes, extracting semantic roles and resolving coreferences, aggregating roles by entity, and projecting those profiles onto a structural scheme. Classical pipelines offer determinism, auditability and low cost but may struggle with colloquial Spanish or corpora without clearly paired turns. Prompted models can handle informal language and nonlinear exchanges, yet their outputs can vary and their assignment decisions may be difficult to audit. The authors view a hybrid as a likely compromise, using classical tools for extraction and aggregation and prompted analysis where interpretive flexibility is most valuable. The framework also remains limited to diagnosing forums: a process that underperforms on these indicators might still shape a wider deliberative system through agenda formation or the transmission of claims to representative institutions.

Ultimately, the article argues that the central obstacle is theoretical rather than computational. More capable models can process language efficiently, but they cannot by themselves determine which linguistic properties carry psychological and political information. The proposed solution is to treat content, interaction and narrative position as complementary lenses, preserve the asymmetry of the synchrony inference, and submit every central claim to possible refutation. Its global falsifier is direct: on a corpus where topic, sentiment, semantic-role distribution and structural position are measured together, if the role and position variables add no interpretive or predictive value beyond topic and sentiment, the framework’s premise fails. Its future therefore depends on annotated corpora of Latin American participation, Spanish-specific function-word schemes, calibrated connotation resources and collaboration among linguists, political scientists, process designers and participating communities.

Subject of Research: A computational framework for analyzing language, interactional synchrony and narrative agency in citizen participation discourse

Article Title: Language, agency, and deliberation: a multilevel theoretical framework for the computational analysis of citizen participation

Article References: Fuentes-Bravo, C., & Raveau, M. P. (2026). Language, agency, and deliberation: a multilevel theoretical framework for the computational analysis of citizen participation. Frontiers in Artificial Intelligence, 9, Article 1878502. https://doi.org/10.3389/frai.2026.1878502

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1878502

Keywords: citizen participation, computational linguistics, computational psycholinguistics, language style matching, semantic roles, deliberative theory, narrative positioning, Spanish language, large language models, democracy, Language, agency