Procedure and participants.

The study was preregistered (https://aspredicted.org/y2kz-s7zk.pdf) and ethical approval was obtained from the Ethical Committee of Roma Tre University (Italy) in February 2024. No deviations from the approved study protocol occurred after approval was obtained (additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information S2 File). The data were collected in one day at the campus (20/05/2024). Participation was voluntary and anonymous: participants were given an identification code when they entered the classroom and received course credit as compensation. All participants provided written informed consent. Data were collected using pencil-and-paper questionnaires without computerized components and participants’ responses were later transcribed by three native Italian speakers.

In accordance with our preregistered plan to collect at least 250 essays, questionnaires were distributed to over 300 students to account for potential dropouts, exclusions, and missing data. A total of 328 Italian-speaking students participated. After applying exclusion criteria—removing participants who did not consent to use their data (N = 15), failed attention checks (N = 26), or did not complete the essay task (N = 2)—the final sample consisted of 285 participants (221 women and 64 men; mean age = 22.25, range = 19–50).

To control for potential order effects, participants were randomly assigned to one of two groups, counterbalanced such that the questionnaire to assess participants’ conspiracism was completed either before or after watching the film. Overall, the experiment lasted about 3 hours.

The film.

Participants watched the film Leave the World Behind [87] and later completed the essay-writing task in which they were prompted to interpret the film by providing their personal understanding and meaning (see Prompt S1 in S1 File). We choose Leave the World Behind because it is not overtly conspiratorial. A conspiratorial reading is only one among several possible interpretations afforded by its narrative. We deliberately chose a non-explicitly conspiratorial film because our aim was not to test the persuasiveness of conspiratorial argumentation (see, e.g., [68]), but to examine whether people high in conspiracy mentality “fill the gaps” of an ambiguous situation, in this case, a fictional narrative.

The film is deliberately open-ended: it features unexplained phenomena, a fractured narrative timeline, and an unresolved ending that invite viewers to infer what is happening “behind the scenes”. Prompting an interpretation of an overtly conspiratorial film would likely have constrained essays to a similar conspiratorial level. What interested us instead were the additional narrative and interpretative elements emerging from top-down cognitive processes, where pre-existing beliefs and expectations guide how individuals interpret ambiguous events.

In fact, our hypothesis is that individuals with a conspiratorial predisposition would be more likely to fill these narrative gaps with conspiratorial explanations, partly because of their higher need for structure and certainty and lower tolerance for ambiguity [22,36–38]. In this sense, the film serves as a useful tool for observing how people project conspiratorial meanings onto an ambiguous story even in the absence of an explicitly conspiratorial storyline.

Participants watched the film dubbed in Italian without subtitles. Italian dubbing is professionally standardized and ubiquitous. For most Italians a dubbed Hollywood film feels as natural as a domestic one [88]. Italians have been steeped in US screen culture since the 1950s, and recent streaming has only deepened that familiarity. Themes such as institutional breakdown, technological fragility, and post-pandemic anxiety resonate well beyond the US context. Decades of media-globalization research [89] show that such cultural proximity allows viewers to appropriate foreign narratives without losing interpretive nuance.

Psychological measures.

To assess individual differences in conspiracism, we employed the Italian version of the 11-item Conspiracy Mentality Scale (CMS) [90], originally developed by [91]. The CMS was chosen because it measures a generic form of conspiracism like the Generic Conspiracist Beliefs scale by [31], but it is more generic (being freed from items involving specific conspiratorial themes such as the government concealing information about UFOs and aliens). Note that we also searched for the conspiracy mentality questionnaire [27] but we could not find a validated Italian translation. In our sample, the CMS demonstrated good internal consistency (). The CMS was originally developed to assess two specific dimensions: healthy skepticism (e.g., “Many things happen without the public’s knowledge”, ) and conspiratorial ideation (e.g., “The truth is known only to a secret powerful group that actively disseminates false information or misleads the public”, ).

To examine the factorial structure of the CMS in our sample, we conducted a confirmatory factor analysis (CFA) using the R package lavaan [92]. We compared a one-factor model—treating conspiracy mentality as a single construct—with a two-factor model distinguishing skepticism from conspiratorial ideation. Model fit indices indicated that the two-factor model provided an excellent and significantly better fit compared to the one-factor model ( = 119.88, p < .001). Although the CMS is best represented by a two-factor structure, this distinction did not substantially alter our main findings regarding the relationship between conspiracy mentality and linguistic measures. Therefore, to simplify the presentation, we report results using the single-dimension structure. Results based on the two-factor model are provided in the Appendix for completeness and transparency (as indicated in our preregistration, we also collected additional psychological measures for exploratory purposes, namely attachment style [93], translated into Italian by [94], and narcissism [95], translated into Italian by [96]; their correlations with conspiracy-related variables are reported in Table S4, see S1 File).

Assessing conspiratorial narrative using LLMs.

As preregistered, we used Large Language Models (LLMs), specifically ChatGPT, to evaluate the conspiratorial narrative content of participants’ essays. LLMs have become increasingly popular in psychological research [97,98], demonstrating their value for text classification outperforming crowd workers [99] even without task-specific training [98]. Indeed, LLMs have shown superior performance compared to traditional dictionary-based analyses when classifying short English texts such as tweets, Reddit comments, and news headlines [100], despite some limitations in differentiating genuine inquiries and sarcastic content from authentic conspiratorial beliefs [101]. Given these considerations, we implemented a two-step validation procedure: first, we assessed LLMs’ reliability in identifying conspiracy-related content in English texts and second, we validated its performance specifically on Italian-language texts.

To evaluate LLMs’ effectiveness to label English texts as conspiracy, we relied on textual material gathered from two corpora: LOCO (the Language Of COnspiracy corpus [57]) and DONALD (the 2M-document Dataset Of News Articles for studying the Language of Dubious information [78]). LOCO is an 88-million-word corpus comprising 23,937 conspiracy and 72,806 mainstream texts focused on 47 well-known themes that have generated CTs (e.g., Princess Diana’s death, the Moon landing, the 9/11 terrorist attacks). DONALD consists of approximately 2 billion words across 2,173,172 news articles—217,703 of which originated from conspiracy websites—covering 172 politically polarized topics (e.g., abortion, immigration, civil rights, climate change).

We prompted ChatGPT by OpenAI—one of the most popular and cost-effective LLMs at the time of writing (in July 2025, ChatGPT’s GPT–4o–mini model cost $0.15 + $0.6 (input + output) per one million tokens, compared to Claude claude–3–5-sonnet that costs $3 + $15 per one million tokens)—to assign a fine-grained conspiratorial language score to each document on a 0–10 Likert scale (from non-conspiratorial language [0] to highly conspiratorial language [10]). Results showed that conspiracy-related documents received significantly higher scores (M = 6.93, SD = 2.61) compared to mainstream texts (M = 1.58, SD = 1.80; , SE = .005, p < .001). Approximately 77% of conspiracy-related documents were rated by ChatGPT above 5, whereas 95% of mainstream documents were rated below 5.

To examine generalizability beyond LOCO’s prominent conspiracy-related themes, we selected from DONALD a subset of 144,000 documents (12,000 per ideological domain, each between 500 and 1,500 words). We then validated ChatGPT’s ratings by computing their correlation with website-level reliability scores obtained from [102]. This analysis revealed a strong negative correlation (r = −.67 [−.693, −.637], p < .001), indicating that texts from less reliable sources received higher conspiratorial scores.

While ChatGPT demonstrated reliability for English texts, its accuracy with Italian-language texts required further validation. We therefore tested its performance using IRMA [79], a corpus of over 600,000 Italian news articles (approximately 335 million words) from 56 websites categorized as unreliable by professional fact-checkers. We selected a subset of 2,714 documents from IRMA (up to 50 per website where available, each under 500 words), which likely included a mix of conspiratorial and non-conspiratorial texts. Each document was scored by ChatGPT for conspiratorial content (again using a 0–10 Likert scale).

To establish a human-coded benchmark, we randomly selected 180 documents spanning the entire range of ChatGPT scores, which were independently evaluated by three native Italian speakers (60 documents per rater) using the same scoring method. We compared ChatGPT ratings (using the gpt-4o-mini model) with those of another LLM, Claude by Anthropic (using the claude-3–5-sonnet model). The correlation between human ratings and LLMs’s ratings was overall robust, indicating strong agreement, although Claude (r₁₇₆ = .701, [.617, .769], p < .001) performed slightly better than ChatGPT (r₁₇₈ = .676, [.588, .748], p < .001; but note that the 95% confidence intervals overlap).

Based on these validation steps, we concluded that LLMs provide reliable evaluations of conspiracism and that Claude performed slightly better than ChatGPT for Italian texts. Thus, we employed Prompt S2 (see S1 File) to score participants’ essays via Claude (note that the use of Claude deviates from our preregistered use of ChatGPT), adopting the definition of CTs provided by [3]. Note that before being acknowledged with Claude, we used ChatGPT to evaluated 108 items previously rated by human coders—MTurk workers and master’s students—for conspiratorial content. The definition of CTs provided by [3] used as prompt for ChatGPT yielded the highest predictive accuracy (R² = .644) compared to three alternative definitions (ChatGPT’s own definition: R² = .639; [2]: R² = .595; and [21]: R² = .637).

Conspiratorial lexicon (dictionary).

Given the lexical convergence of conspiratorial discourse observed across social media [54,55] and online texts [57], we explored whether participants’ essays similarly reflected a conspiratorial lexicon corresponding to their level of conspiracism. To this end, as an alternative to LLM, we employed a dictionary-based approach for assessing lexical conspiracism. A dictionary is a predefined list of words associated with a specific psychological construct or phenomenon. Widely used in psychological research [98,109,111], this method consists in identifying and counting occurrences of words in a text that match entries in the dictionary. To ensure comparability across documents of varying lengths, the frequency of matches is expressed as a ratio ranging from 0 (no dictionary matches) to 1 (all words matched), allowing for standardized comparisons between texts.

We are currently developing a data-driven dictionary of conspiracism in English [112]. Unlike LLMs—which may depend on narrative structure and textual coherence—the dictionary method quantifies conspiratorial content solely based on word frequency, independent of word order or narrative cohesion [98]. The dictionary used in this study comprises 300 English lemmas derived from the topic-matched structure of LOCO [57]. Specifically, by contrasting conspiracy with mainstream texts within each seed topic in LOCO (e.g., the death of Princess Diana), we employed a term frequency–inverse document frequency (TF-IDF) approach to isolate conspiracy-specific terms while filtering out topic-specific terms treated as stop words (e.g., car crash and Paris in the context of the death of Princess Diana). As such, this method effectively identifies lexical markers of conspiracism that generalize across diverse topics. To validate our dictionary, we applied it to the subset of 144,000 documents from DONALD previously scored for conspiratorial content using ChatGPT (as described above). The dictionary-derived scores significantly correlated with ChatGPT ratings (r = .60, [.597, .605], p < .001).

For this work, the dictionary was translated into Italian using ChatGPT (see Prompt S3 in S1 File). The complete list of Italian dictionary terms is provided in the S1 File (see Section S2). The Italian dictionary was then applied to the lemmatized version of our combined corpus from Studies 1 and 2. Lemmatization was performed using the R package spacyr [113], a wrapper for the Python library spaCy [114], which provides accurate extraction of grammatical structure and prevents potential mapping errors (e.g., distinguishing between the Italian noun mondo [world] and the verb mondare [cleanse]). We selected the pretrained model it_core_news_lg due to its superior performance among available Italian models in spaCy.

After lemmatization, each text was reconstructed by concatenating its lemmatized tokens. Tokenization was subsequently performed using the R package quanteda [115], removing punctuation, symbols, and numbers. Two punctuation marks (? and !) were retained, as they appeared in the conspiratorial dictionary. Compound words were also combined (e.g., sito web became sito_web [website]). Finally, the dictionary was applied to the processed texts, computing the proportion of conspiratorial words in each document. The resulting scores ranged theoretically from 0 (no matches) to 1 (all words matched).

Sophistication.

Conspiracy narratives often feature high levels of linguistic sophistication, characterized by complex syntax and specialized vocabulary. Linguistic sophistication can serve persuasive goals [116,117], strategically creating impressions of truthfulness, competence, and expertise within conspiratorial discourse [73]. Empirical research suggests that conspiracy-related texts generally exhibit greater lexical and syntactic complexity compared to non-conspiracy texts [63,118,119]. It is thus plausible to expect similar linguistic sophistication in offline contexts, such as in the essays analyzed here, as a function of individual levels of conspiracism. However, considering that conspiracy believers typically demonstrate lower verbal intelligence scores [120] and tend to have less formal education [121], linguistic sophistication may not straightforwardly correlate with conspiratorial belief. In the following, we describe how we operationalize linguistic sophistication using two complementary dimensions: syntactic and lexical sophistication.

Note that beyond syntactic and lexical sophistication, previous research has also examined how semantic and pragmatic features (e.g., counterfactuals, hypotheticals, and evaluative language) contribute to conspiracy discourse, particularly in relation to refutational reasoning [122]. While these aspects fall outside the scope of our structurally focused analysis, we acknowledge their relevance and encourage future work to incorporate verb semantics and discourse-level patterns for a more comprehensive understanding of conspiratorial rhetoric.

Syntactic Sophistication.

To measure syntactic sophistication, we extracted three key metrics from the parsed texts of participants’ essays. First, we calculated the average sentence length by counting the number of tokens per sentence and computing the mean sentence length for each participant, with longer sentences indicating greater syntactic complexity. Second, we measured the average clauses per sentence, defined by counting all verbs (including auxiliaries) as proxies for clauses and computing the average number of clauses per sentence for each participant; a higher average suggests more complex clausal embedding. Third, we determined the average dependency tree depth by calculating the maximum depth of dependency structures in each sentence (i.e., the absolute positional difference between each token and its syntactic head) and averaging these maximum depths across all sentences per participant. Greater tree depth means greater hierarchical syntactic complexity.

To create a unified measure of syntactic sophistication, we combined these three metrics using Principal Component Analysis. The first principal component accounted for approximately 91% of the variance and served as our composite measure of syntactic sophistication, effectively capturing multiple aspects of syntactic complexity in a single score. We validated our syntactic sophistication measure using a synthetic corpus specifically created by ChatGPT (N = 50 documents) containing texts of explicitly high or low syntactic complexity (see Prompt S4 in S1 File) As expected, the high-complexity texts scored higher in syntactic sophistication compared to low-complexity texts (t_44.61 = 9.18, p < .001, Cohen’s d = 2.60).

We applied our syntactic sophistication index to the LOCO corpus. We found that, consistent with prior findings on social media [118,119], conspiracy documents showed greater syntactic complexity than mainstream texts (, [.05, .08], SE = .01, t = 8.34, p < .001; using a multilevel regression models via the lme4 and lmerTest packages in R [123,124], including random intercepts for k = 200 topics, thus ensuring within-topic comparisons).

Lexical Sophistication.

To our knowledge, no existing word norms directly quantify lexical sophistication. Instead, researchers have often inferred lexical sophistication indirectly via metrics such as processing time that is known to correlate with age of acquisition (AoA; [125]), word frequency [126], and word length [127]. To address this gap, we generated lexical sophistication norms using LLMs, which have shown promising results in approximating human psycholinguistic judgments [128]. Specifically, we extracted a list of Italian lemmas and their part-of-speech tags from the IRMA corpus [79], and prompted ChatGPT to estimate the complexity of each word analogously to AoA ratings. We prompted ChatGPT to assess how sophisticated a word would appear to a native Italian speaker (see Prompt S5 in S1 File). We anchored the ratings in AoA norms because they reflect the developmental trajectory of lexical acquisition: early-acquired words (e.g., mum, food) are typically perceived as less complex, whereas later-acquired or specialized terms (e.g., syphilis, kerosene, neurotic) are typically perceived as more complex. AoA norms have previously been used as a proxy for linguistic sophistication [129] and have captured sophistication in nominal compounds in conspiracy texts [63].

We obtained sophistication ratings for 190,507 Italian lemmas. These ratings showed strong convergent validity with the existing Italian AoA dataset [130], yielding a correlation of r = .64 [.611, .665], p < .001 across the 1,912 overlapping lemmas. Using the same prompt, we also generated complexity norms for English and German (each with N > 100,000 lemmas). These norms similarly correlated well with established AoA datasets (in English: r = .74, based on 28,000 words from [125]; in German: r = .71, based on 2,958 words from [131]). Note that while these correlations are encouraging, the cognitive mechanisms underlying LLM-generated norms remain opaque. Future work should explore whether these norms exhibit the same cross-linguistic consistency as human AoA ratings [132]. Applying the English LLM-based norms to the LOCO corpus, we found that conspiracy texts exhibited significantly higher lexical sophistication than mainstream texts (, [.06, .09], SE = .01, t = 9.76, p < .001; using multilevel regression with a random intercept for k = 200 topics).

Here, for participant essays, we computed lexical sophistication by mapping the generated Italian AoA ratings onto each lemmatized text and calculating the mean per participant.

Megalalia.

It is possible that linguistic sophistication in conspiratorial discourse is not uniformly distributed. Rather, conspiracy authors might selectively employ specialized terminology across diverse and unrelated contexts [63], even within texts that are otherwise linguistically simple. In this view, conspiratorial linguistic sophistication could be defined by an uneven distribution of sophisticated terms within the discourse.

We are currently developing a metric designed specifically to quantify the disproportionate use of sophisticated vocabulary [77]. We label this phenomenon as megalalia (from Greek mega = “big” and lalia = “speech”), referring to the inappropriate use of highly sophisticated vocabulary without regard for context or necessity. This linguistic behavior often serves the strategic purpose of creating an illusion of intelligence, credibility, or authority [73,117].

The concept of megalalia relies on the assumption that sophisticated words, when used out of context or unnecessarily, should stand out as disproportionately complex relative to the surrounding lexical context. To quantify megalalia, we applied the Gini coefficient to the distribution of lexical sophistication scores within each text. Originally developed to measure economic inequality, the Gini coefficient quantifies disparity within a distribution. Applied to lexical sophistication, a Gini coefficient of zero would reflect complete uniformity indicating equal lexical sophistication across the entire text (e.g., pizza, pasta, table), whereas values approaching one would suggest high inequality, i.e., a small subset of sophisticated words contrasts sharply with otherwise simpler vocabulary (e.g., pizza, pasta, orbitofrontal cortex). This pattern of selectively inserting sophisticated terminology could reflect attempts to exaggerate perceived expertise or credibility, characteristic of conspiratorial arguments [73]. Testing this measure on the LOCO corpus revealed significantly higher levels of megalalia in conspiracy texts compared to mainstream texts (, [.25, .28], SE = .01, t = 32.78, p < .001; using the same multilevel model structure as described previously).

Cohesion.

A large-scale network analysis of conspiracy texts using LOCO revealed that conspiratorial discourse exhibits lower textual cohesion compared to mainstream texts, characterized by frequent co-occurrences of unrelated topics [58]. Similar fragmented and associative thought patterns have been observed among individuals on the schizophrenia spectrum [133–135], which partially overlaps with conspiracism (schizotypy is the strongest psychopathological predictor of conspiracism [24]). Based on these observations, we examined whether textual cohesion in participants’ essays negatively correlated with their level of conspiracism. Because previous assessments of cohesion in conspiracy-related texts were conducted exclusively in English [58], here, we developed a novel approach to measure textual cohesion in other-than-English languages.

We quantified textual cohesion as the semantic similarity between consecutive sentences within each document. To this goal, we employed word embeddings from the Italian pre-trained fastText model [136,137] (300 dimensions; https://fasttext.cc/docs/en/crawl-vectors.html), which offer distinct advantages for morphologically rich languages like Italian by leveraging subword information [138]. Each sentence was represented as the mean vector of its constituent word embeddings, after removing stopwords (list of 279 stopwords from quanteda; [115]) and punctuation. Sequential textual cohesion scores were then computed by calculating the cosine similarity between embeddings of adjacent sentences within each text. Higher sequential cohesion values indicate greater semantic similarity between consecutive sentences, reflecting more coherent and structured discourse.

To validate our cohesion measure, we adopted a procedure similar to that used in previous research [58]. We compared cohesion scores of original texts to their scrambled counterparts, in which sentence order was randomized while preserving word order within sentences. Specifically, we selected a random sample of 100 documents, each containing at least 20 sentences, from the IRMA corpus [79]. As expected, the scrambled documents exhibited significantly lower sequential cohesion than the original documents (t_186.51 = 5.63, p < .001, d = .80), confirming our metric’s sensitivity to disruptions in textual continuity. Further validation using the English fastText model was performed on a synthetic dataset (N = 770) from prior work (see section Testing cohesion metrics in [58]), yielding similarly results (t_723.91 = 16.19, p < .001, d = 1.17).

The stimulus.

We observed no differences in conspiracism between participants who completed the questionnaire before versus after watching the film. This result aligns with previous research, which found that exposure to an episode of The X-Files did not increase participants’ conspiracy mentality [140]. As discussed by the authors, the lack of effect might reflect the perceived plausibility or factual grounding of the film (e.g., in contrast to JFK [141] or the Chernobyl nuclear power plant explosion [68], which did successfully influence conspiratorial beliefs). Our goal, however, was not to influence or measure participants’ attitudes, but rather to observe their sense-making activity. Fictional narratives are routinely used as vehicles for commenting on real-world institutions and power relations [142,143]. As such, regardless of the the perceived realism of the film, conspiratorial interpretations can arise at a literal or allegorical level, or through analogies with what participants perceive as the real world. Thus, if conspiracism shapes interpretation, it should remain detectable in essays about a fictional scenario, even though the openness of the task also allowed for non-conspiratorial readings and may therefore have lowered the base rate of explicitly conspiratorial narratives. Nevertheless, it is also possible that participants interpreted the film as a work of art rather than an account of actual or possible events. In this case, they may have understood the instructions as an invitation to provide a film-criticism-style response, focusing on the director’s intentions and overarching message instead of the narrative content per se.

Participants’ motivation.

A lack of participant motivation might also explain our null findings. Although we did not directly measure motivational factors, we indirectly examined participants’ motivation through essay length. We did not observe a positive relationship between conspiracism and essays’ word count. This is not trivial because prior research consistently shows that conspiracy-related content on websites [57,78–80] and social media platforms (e.g., Reddit; [55]) tends to be longer. Longer texts are generally associated with greater persuasive intent [81].

One plausible explanation for this discrepancy relates to the context of our study. Unlike naturalistic online environments, where individuals are intrinsically motivated to persuade, our participants may have lacked a similar motivation. Online conspiracy promoters typically engage in persuasive communication: even when their personal belief in conspiracies might differ from their spreading behavior, their motivation to disseminate conspiratorial narratives remains strong [144,145]. In contrast, participants in our study likely viewed the task primarily as a requirement for earning course credit, rather than as an opportunity to persuade. Without the social context or intrinsic desire to persuade others, participants may have been less motivated to elaborate their thoughts or write extensively about conspiratorial themes.

This motivational limitation is consistent with broader challenges in eliciting conspiratorial texts within controlled laboratory environments. Settings such as classrooms or online research platforms (e.g., Prolific, MTurk) typically lack the social dynamics crucial to the formation and propagation of CTs [1,2,146]. In naturalistic settings, such as social media, personal blogs, or conspiracy websites, individuals are strongly motivated to construct and disseminate elaborate conspiratorial content, especially on topics they perceive as personally meaningful. For example, anti-vaccination advocates often create detailed narratives emphasizing perceived dangers, systemic corruption, or threats to personal freedom, driven by the desire to persuade, connect with like-minded individuals, or influence public opinion. In contrast, when participants are asked, as in our lab-based study, to interpret a film for course credit, their motivation likely shifts toward minimal task completion rather than engaging deeply or persuasively with conspiratorial content—even if they hold conspiratorial beliefs.

Finally, it cannot be ruled out that the fear of social stigma often associated with conspiracy beliefs [147] may have played a role among the motivational factors. Our participants were indeed students who were aware that their texts would be eventually read by others. Despite the anonymity of the data collection process, which was meticulously designed to avert such risks, the possibility of self-censorship cannot be discounted [148].

The prompt.

Beyond motivational factors, another potential explanation for our results relates to the nature of the prompt used to elicit participants’ essays (see Prompt S1 in S1 File). The prompt was intentionally designed to trigger cognitive biases associated with conspiracy mentality, known to thrive under conditions of ambiguity and uncertainty [36,37]. However, CTs also fulfill broader epistemic, existential, and social needs [1,2]. Notably, CTs frequently provide explanatory frameworks helping individuals make sense of complex, uncertain, or threatening situations, thus satisfying an epistemic need. It is possible that our prompt, which merely asked participants to “interpret” the film, did not effectively activate this explanatory need. Would our results have differed if the prompt had explicitly engaged participants’ epistemic motivations? For instance, rather than asking participants to interpret the film, prompting them to causally explain the depicted events (e.g., “Why did these events occur?” or “Who or what is responsible for these events?”) might have shifted their responses from subjective interpretation toward structured explanations, potentially eliciting narratives more consistent with conspiracism.

Cognitive demand.

One plausible explanation involves the cognitive demands associated with narrative construction. The use of familiar or emotionally charged words—particularly those with ideological significance—can occur relatively automatically. In contrast, crafting a coherent narrative involves higher-order cognitive processes such as causal reasoning, temporal sequencing, and thematic integration, all of which are more cognitively demanding [149]. Constructing a narrative necessitates satisfying multiple constraints, including maintaining cohesion and coherence and providing plausible causal explanations for events. Moreover, a high conspiracy mentality does not necessarily imply the ability or motivation to produce fully elaborated conspiracy narratives. Creating such narratives requires advanced causal reasoning and narrative-construction skills that individuals high in conspiracism may not consistently possess or be motivated to apply [120,121]. Thus, participants with higher conspiracy mentality in our study may have used conspiratorial vocabulary (e.g., deception, cover-up, elite) without integrating these words into complete conspiratorial storylines.

Exposure.

Alternatively, our dictionary-based approach may have picked participants’ habitual exposure to conspiratorial discourse. This interpretation aligns with prior evidence indicating higher lexical similarity among conspiratorial webpages across widely disparate topics [58]. Participants might have used recurring idiomatic expressions or lexical patterns (e.g., “The truth is hidden”, “Pulling the strings” [118]) that, despite not being sufficient individually to form a coherent conspiratorial narrative recognizable by the LLM, still contributed to the conspiratorial lexicon score. Therefore, individuals high in conspiracism may naturally adopt linguistic patterns regularly encountered in online conspiracy communities. Indeed, lexical overlap exists between conspiratorial discourse observed on social media [54,55] and conspiracy-focused websites [57]. Thus, it is reasonable to expect that these lexical patterns could transfer into offline, spontaneous writing, even when participants are neither addressing an audience nor actively trying to persuade.

Cohesion.

Previous research has found lower cohesion in conspiratorial webpages compared to mainstream ones [58]. Here, however, we observed no such association in participants’ short essays. One explanation for lower cohesion in online conspiracy narratives is that they typically jump between multiple unrelated themes, creating highly interconnected but topically scattered narratives [58]. Some scholars have characterized this accumulation of disparate “evidence” (also known as the mille-feuille argumentative style or the Gish gallop) as a rhetorical strategy designed to overwhelm readers by rapidly presenting multiple arguments without concern for their accuracy or logical coherence [73,150–152]. Therefore, persuasive motivation likely plays a key role in reducing textual cohesion online: to convince others, online conspiratorial narratives strategically connect diverse and unrelated topics, which results in a decreased cohesion.

As previously suggested, our participants may have lacked similar persuasive motivations. The task (writing essays prompted by an ambiguous film), the context (lab study rather than online), or the prompt itself may not have triggered sufficient motivation to persuade. Consequently, participants were unlikely to employ the wide-ranging rhetorical strategies characteristic of online conspiratorial discourse, resulting in similar cohesion levels irrespective of their conspiracy beliefs.

Alternatively, the absence of association may be language-specific. Italian is less prevalent online than English, potentially resulting in fewer resources for pre-training Italian embeddings and possibly impacting embedding quality. Additionally, Italian’s highly inflectional nature could have influenced the sensitivity of our cohesion metric. Consistent with this interpretation, our validation procedure yielded a larger effect size (the Cohen’s d) for cohesion in English than in Italian, suggesting potential language-specific limitations.

Lexical sophistication.

In previous analyses on LOCO, conspiracy-related texts employed more lexically sophisticated terms compared to mainstream documents [63]. However, in our study, lexical sophistication negatively correlated with participants’ conspiracism. This unexpected result might relate to prior research indicating that higher conspiracism often correlates with lower levels of education [121], potentially leading individuals to use less sophisticated vocabulary. However, our participants typically had at least some university-level education, and cohesion was not associated with education levels within our sample, suggesting that this explanation might not fully apply here. An alternative interpretation is that in online conspiracy discourse, sophisticated vocabulary might be employed strategically to signal expertise and authority [73], reflecting persuasive intent. As mentioned above, such motivation was possibly absent among our participants. Without persuasive intents, our participants might not have felt compelled to use sophisticated terminology. This could tentatively explain the observed negative correlation.

Megalalia.

Megalalia—the uneven distribution of sophisticated vocabulary—was positively associated with higher conspiracism in both our participants’ essays and in LOCO. This preliminary finding suggests that megalalia might be a consistent linguistic feature of conspiratorial discourse, appearing across both online and offline contexts. However, it is somewhat puzzling to observe megalalia in our sample, given participants’ presumed lack of clear persuasive motivation. One potential explanation, beyond persuasion, taps into the need for uniqueness, a trait consistently associated with conspiracism (e.g., [72]) and closely linked to personal and social identity [18]. Individuals may thus selectively employ unnecessarily sophisticated vocabulary not only to persuade but also to project an image of exceptional intelligence, competence, or uniqueness [153]. Megalalia might similarly serve to evoke impressions of depth or hidden knowledge, paralleling the pseudo-profound style associated with bullshit receptivity and conspiracism [154]. Consequently, even without overt persuasive intent or an explicitly conspiratorial narrative, individuals with higher conspiracism might still exhibit megalalia as a form of identity signaling or uniqueness-seeking behavior.

Syntactic sophistication.

Syntactic sophistication correlated positively with conspiracism in our sample, aligning with previous findings that conspiracy-related accounts on social media [118,119] and conspiracy documents in LOCO also exhibit greater syntactic complexity. Given that a lack of motivation could plausibly explain other unexpected findings (e.g., cohesion), it is intriguing that syntactic sophistication was higher among participants with greater conspiracism. One possibility is that higher syntactic complexity emerges from longer, convoluted sentences characteristic of a stream-of-consciousness style. This interpretation could be consistent with evidence linking conspiracism to thought disorders typically associated with schizophrenia [24,133–135]. However, this interpretation contrasts with findings that individuals with schizophrenia generally exhibit reduced syntactic complexity [155]. Thus, even assuming limited motivation among participants, it remains unclear why syntactic complexity would positively associate with conspiracism and which cognitive or motivational mechanisms might drive this relationship. Impression management, as we discussed for megalalia, could be a tentative explanation.

LLMs’ classification.

A first limitation concerns the prompt we used to elicit LLM judgments, which did not include explicit anchors. Unlike our lexical sophistication norms, where we provided concrete exemplars (e.g., mum vs syphilis), here neither the models nor human raters were given examples illustrating different levels of conspiratorial strength. In the absence of such anchors, it is difficult to calibrate what counts as a “very strong conspiratorial narrative”. Future work relying on LLMs’ classification could test whether adding explicit anchors or benchmark texts improves the reliability and validity of these assessments.

A second limitation is inherent to the classification method itself. LLMs may reliably detect conspiratorial narratives primarily when they appear in clearly structured and coherent texts [107,108]. If models are more effective when narratives are cohesive, they may fail to identify conspiratorial content expressed in fragmented, tangential, or loosely organized ways. This interpretation is indirectly supported by our exploratory analyses, which indicated that LLM-based assessments were moderated by indices of textual quality. By contrast, a simpler dictionary measure based on word count successfully captured the relationship between conspiracism and conspiratorial vocabulary use, suggesting that LLM classifications may be more sensitive to narrative coherence than to conspiratorial content alone.

A third additional concern is that LLMs can themselves exhibit a degree of conspiratorial bias [156]. If a model treats conspiratorial claims as relatively plausible, an essay containing such content might receive a lower conspiratorial rating than warranted, resulting in a biased assessment. Such bias, however, should be evenly distributed across the rating continuum and thus systematically decrease conspiratorial ratings for all texts. This would not be problematic unless an artificial floor effect is detected.

Finally, there is a broader concern on how LLMs have been trained. Depending on training material, topics and genres representations are skewed or biased (see, e.g., differences between Google books corpus and British periodicals [157]). This is a well known problem of representability in corpus construction [158], but it can represents a major problem in regard to LLMs, which are often trained on large proprietary corpora whose exact composition is undisclosed, complicating replication and comparability across studies [159].

LOCO and DONALD’s limitations.

Because the conspiracy dictionary used to extract the conspiratorial lexicon was built on LOCO, which also served as a validation corpus for our measures, some aspects of LOCO’s construction need to be clarified to flag potential limitations. First, the conspiracy dictionary is not fully independent of the LLM-based assessment. Although it was constructed in a data-driven fashion from LOCO extracting conspiracy-related terms (e.g., deception) stripped from event-base ones (Paris, Diana), its validation relied on individual documents from DONALD, whose ground truth labels were defined by ChatGPT. This partial overlap may introduce a degree of circularity between feature construction (the dictionary) and the evaluation criterion (LLM judgments).

Second, LOCO was built around events that are known to have generated CTs, gathered from surveys measuring belief in specific CTs [160,161]. However, it could be argued that not all of these events can be unequivocally considered CTs. For example, there is nothing inherently harmful about Bigfoot itself [162], or about the (alleged) faked deaths of popular figures such as Elvis Presley and Princess Diana (or Paul McCartney). Although these events can be considered as facts allegedly known by authorities but hidden from the public (consistent with the definition proposed by [163]), they seem to lack the clear malicious intent that is often taken to characterize CTs [2,3]. This ambiguity in the seed events may limit the content validity of LOCO as a corpus of genuine conspiratorial documents and, by extension, of the dictionary derived from it.

Third, beyond content validity, not all documents in LOCO actually contain CTs, with a portion of documents that mention the event’s keyword (e.g., “Sandy Hook”) without any conspiratorial discussion of the event itself (the Sandy Hook school shooting) [164]. This reflects a well-known problem in corpus collection [101], namely a trade-off between sample size and accuracy. On the one hand, gathering all documents from a source known to deliver CTs ensures a large sample size but increases the likelihood of false positives, as it also includes documents that do not mention CTs [164]. On the other hand, false positives are minimized if documents are carefully selected to include CTs, but this results in a costly and time-consuming endeavour, with limited sample size and heterogeneity, and may introduce sample bias if documents are selected based on the same lexical features that are later used to analyze their lexical content. These corpus-level constraints should be kept in mind when interpreting the performance and generalizability of both the dictionary and the measures derived from it.

Other psychological contributors.

In this work, we focused on conspiracism as a unique predictor of the emergence of conspiratorial narratives. However, it must be acknowledged that other psychological traits may also contribute to the production of such narratives. Below, we highlight several candidates that future research could explicitly incorporate alongside conspiracism.

Schizotypy—a subclinical form of schizophrenia characterized by paranoia, magical ideation, and unconventional thinking—has been identified as one of the strongest psychopathological predictors of conspiracism [24]. Language on the schizo spectrum, including both schizophrenia and schizotypy, has been described as vague, circumstantial, metaphorical, overelaborate, and stereotyped, with loose semantic associations [165,166], heightened sensitivity to associative and irrelevant stimuli [167,168], reduced semantic cohesion, and deficits in temporal, causal-motivational, and thematic coherence [133–135]. Schizotypy could therefore help explain some narrative and linguistic features frequently observed in CTs, such as high thematic heterogeneity, hyper-interconnectedness, loose semantic associations, and lower topic specificity and lexical cohesion [58–61,63].

Magical thinking, which overlaps with both schizotypy and conspiracism [169], is particularly connected to causality and agency—two core ingredients of conspiracy theorizing [45–47]. This trait may facilitate the endorsement of highly improbable or counterintuitive CTs, such as the belief in omnipotent and omniscient agents (e.g., Reptilian shape-shifters [43]). In the mind of magical thinkers, conspirators may be perceived not only as malevolent but also as virtually flawless in their activity. Even socially marginalized or low-status groups such as immigrants are sometimes portrayed in conspiratorial narratives as possessing disproportionate power to destabilize entire nations, “impose their rules”, or orchestrate demographic takeovers. This perceived omnipotence, applied to groups typically seen as relatively powerless, exemplifies the magical and counterintuitive logic underlying many CTs and coherently sustains a narrative of a vast and hidden agenda.

Conspiracy believers are also more susceptible to probabilistic reasoning errors and have a biased conception of randomness in which coincidences are seen as suspicious rather than expected [33,42,170–172]. As such, they may disregard the probabilistic reality that large-scale conspiracies are prone to failure due to human error and increasing exposure risks as the number of participants grows [173]. One probabilistic error in particular such as the overestimation of the likelihood of co-occurring events—the conjunction fallacy—may be especially relevant for the generation of conspiratorial narratives, as it could help explain the construction of hyperconnected and thematically heterogeneous narratives that bind together otherwise unrelated events.

Another potential predictor of the emergence of conspiratorial narratives is belief in specific CTs. Although conceptually related, correlated, and often used interchangeably by researchers [174], belief in CTs and conspiracy mentality are distinct psychological constructs [22,174–177]. Conspiracy mentality reflects a general tendency to distrust authority and suspect hidden motives, typically assessed with abstract or non-specific statements, and may be more closely associated with general skepticism, political cynicism, and information-seeking motives. Conversely, belief in specific CTs involves endorsement of concrete and often epistemically risky claims and is more strongly linked to pathological cognitive tendencies, such as magical thinking, schizotypy, or reduced analytic reasoning [177,178]. Given their more concrete and assertive nature, specific CTs may represent a stronger predictor than conspiracy mentality for the generation of conspiratorial narratives, which typically require elaboration, detail, and a degree of belief commitment that aligns more naturally with endorsement of specific, risky claims than with generalized skepticism alone. Our effect sizes support this conjecture: the relationship between the essays’ level of conspiratorial narrative and conspiracism was higher for the more epistemically risky generic conspiracist scale [31] compared to the conspiracy mentality scale [91].

Finally, motivated cognition, partisanship, and affective polarization may also be important contributors. CTs frequently denigrate political or social opponents [53,179,180]; this applies, for example, to QAnon [181–184] and to partisan-oriented CTs about climate change endorsed on both the left and the right [185]. Importantly, CTs can be endorsed or propagated for reasons of convenience [179]. They are not necessarily generated only by sincere believers but can be deliberately constructed and disseminated to achieve strategic goals, such as influencing political behavior or generating profit [144,145]. For instance, many of Donald Trump’s conspiratorial claims lack evidentiary support and may represent a type of reasoning that is irrational, internally inconsistent, or performative rather than sincerely believed [186,187]. An important avenue for future research is thus to determine the extent to which CTs are produced intentionally, independent of genuinely held beliefs, versus emerging organically from cognitive biases and dispositional tendencies such as conspiracism, schizotypy, magical thinking, and probabilistic reasoning errors.

Cumulative effect.

As our data appear to suggest, the actual impact of conspiracism on the emergence of conspiratorial narratives may be relatively small and, as discussed, contingent on other traits and motivations. Given that CTs do exist, how do they originate and propagate? It is possible that individual contributions to conspiratorial discourse might be modest, yet fully fledged CTs could emerge cumulatively [188]. Initial ideas may gradually develop into elaborated narratives through repeated social interactions among multiple individuals over time [59,62,189,190]. The QAnon movement illustrates this process, encapsulating many specific beliefs—even conflicting ones—without a single official version [181,182]; narratives are generated, recombined, and personalized, where individual believers assemble their own conspiratorial “package”. Even before QAnon, analyses of the subreddit r/conspiracy similarly suggested a decentralized, cumulative pattern of narrative construction [59]. Future research could investigate this cumulative hypothesis by employing paradigms inspired by cultural transmission studies [86,191], allowing researchers to examine how narratives evolve and transform across repeated social exchanges.

Future directions.

Although our work paves the way for understanding the potential link between conspiracism and language production, several questions remain open, particularly in light of the methodological limitations of our current approach. A recurrent issue in our discussion concerns the extent to which conspiracism overlaps with, or diverges from, persuasive intent. Future experimental studies could systematically manipulate participants’ motivation to persuade (e.g., explicitly instructing participants to convince an audience) and examine how persuasive intent interacts with conspiracism. Such studies would help clarify whether the linguistic markers identified here (e.g., megalalia, sophistication) emerge specifically due to persuasive motives, conspiratorial cognition, or a combination of both.

A related open question is whether certain linguistic features are context-dependent—activated only by relevant topics or motivational factors—or whether they reflect stable traits of individuals high in conspiracism across diverse settings. Experimental manipulations of topic relevance or incentives to persuade could shed light on whether conspiratorial language emerges only in specific contexts or consistently characterizes the language production of highly conspiratorial individuals, regardless of situational factors.

More comparative research is also necessary to examine how conspiratorial discourse emerges across different venues (e.g., online webpages, social media platforms, lab-based tasks). Applying consistent analytic approaches across these various contexts—as we did by comparing LOCO documents with our participants’ essays—may reveal whether linguistic markers of conspiracism remain stable or differ systematically across communication environments.

Although we did not find evidence that conspiracism leads to overt conspiracy narratives, we observed that participants high in conspiracism used a greater amount of conspiratorial lexicon. This suggests that conspiracism might manifest primarily at the lexical level, rather than through well-structured narratives. If explicit conspiracy narratives do not strongly originate from individuals with high conspiracy beliefs, then who is actually authoring the CTs widely encountered online?

It remains plausible that at least some CTs are strategically constructed—often for political, ideological, or economic gain—rather than arising solely from genuine believers’ worldviews and subsequently amplified via social interactions. Future studies employing paradigms such as cultural transmission experiments, collaborative storytelling approaches, or longitudinal analyses could help determine whether conspiratorial narratives emerge cumulatively from individuals’ cognitive biases or whether they are predominantly products of intentional persuasion strategies by strategically motivated actors.