The 2024 US presidential election unfolded in an environment of heightened polarization, widespread distrust, and unprecedented public anger. Drawing on the Anger Activism Model (AAM), we examined this period of history to assess how anger interacts with external political efficacy to shape opposition toward democratic behaviors and support for partisan and political violence. Using data from a rolling cross-sectional national survey of US adults (N = 1713) conducted 5 weeks before the election and 3 weeks after, we found anger predicted tolerance for undemocratic behavior and violence during weak external political efficacy. At low external efficacy, increases in anger were associated with greater support for undemocratic practices and partisan violence. At high external efficacy, only moderate levels of anger were associated with these destructive outcomes. These findings extend the AAM to antisocial political orientations and highlight efficacy as a critical buffer against democratic backsliding in periods of political volatility.

The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models. Across 700 statements drawn from five published deception datasets, four large language models (gpt-4o, claude-sonnet-4-6, ollama/llama3, deepseek-v4-flash), and two run-types (RAG vs. baseline), a total of 39,200 deception judgments were rendered. Detection accuracies were consistent with typical human accuracies and not statistically different across RAG (54.5

The theory of communicative (dis)enfranchisement (TCD) outlines that the deprivation of a person's rights and privileges can be facilitated via disenfranchising talk (DT) from a critical perspective. However, the social and psychological correlates of DT remain unknown. Therefore, to address this gap, across two studies, we conducted computational analyses of language data to supplement the foundational qualitative research using this theory. We extended the TCD by examining the language used by female chronic pain patients (total N = 923) when reporting on negative encounters with their clinicians which is an established context for examining DT. Women who reported more DT described their negative experiences with their clinicians in more words, more emotional terms, and they worked through their disenfranchisement experiences less psychologically than those reporting less DT. These results are the first to examine and extend the TCD computationally and are a foundation for practically decreasing DT.

The current paper presents conceptual replication attempts of Schindler and Reinhard (2015), who found a negative association between belief in a just world (BJW) — the positive illusion that the world is a just and orderly place — and deception detection accuracy. Two studies, one with undergraduates (Study 1 N = 176) and the other with members of the general public (Study 2 N = 352), failed to obtain this negative relationship and instead found a null effect. Exploratory analyses observed that in the general public sample, BJW was positively associated with truth-bias (one's tendency to ascribe a message as honest independent of its actual veracity). These findings were obtained using aggregated participant-level results and trial-level results. In sum, individual differences like belief in a just world are faintly related to deception detection accuracy at least in the current research settings, though future work would benefit from boundary condition testing.

News organizations shape public discourse at scale, yet relatively little is known about how their communication styles have evolved over time. We analyzed over 350,000 transcripts from three major US cable news networks (i.e., MSNBC, CNN, and Fox News) spanning more than two decades to examine how such organizations “think” over time. Results revealed a stark shift in the communicative patterns of Fox News beginning around 2016–2017, where broadcasts became substantially more analytic and formal while declining in cognitive processing, suggesting a more settled and less meaning-making orientation toward the news. Language Style Matching analyses further suggested Fox News developed greater alignment with the communicative style of the president after 2017, regardless of party in office. These findings offer novel, large-scale evidence that news organizations differ in how they think and work through issues of the day via language patterns, with implications for audience persuasion, trust, and public discourse.

The current paper presents conceptual replication attempts of Schindler and Reinhard (2015), who found a negative association between belief in a just world (BJW) — the positive illusion that the world is a just and orderly place — and deception detection accuracy. Two studies, one with undergraduates (Study 1 N = 176) and the other with members of the general public (Study 2 N = 352), failed to obtain this negative relationship and instead found a null effect. Exploratory analyses observed that in the general public sample, BJW was positively associated with truth-bias (one’s tendency to ascribe a message as honest independent of its actual veracity) and both raw and signal detection theory metrics produced consistent results. In sum, individual differences like belief in a just world are faint and generally unrelated to deception detection accuracy at least in the current research settings, though future work would benefit from boundary condition testing.

As artificial intelligence (AI) becomes increasingly embedded in social life, understanding its interpersonal and psychological implications is urgent yet under-theorized. This paper introduces the Machine-Integrated Relational Adaptation (MIRA) model, a transdisciplinary, middle-range theoretical framework that provides a foundational account of when, how, and why AI functions as a relational entity in human ecosystems. MIRA distinguishes two crucial roles of AI: relational partner (direct interaction companion) and relational mediator (shaping human-to-human communication). Synthesizing psychosocial theories of human relationships, interpersonal communication theory, psycholinguistics, and human–computer interaction, MIRA structures AI's relational impact within antecedents, processes, moderators, and outcomes. Central to MIRA are four principles describing how AI fosters social adaptation: linguistic reciprocity, psychological proximity, interpersonal trust, and relational substitution versus enhancement. These illuminate how adaptive AI language and behavior can elicit emotional investment, simulate mutual understanding, or even supplant human interaction. MIRA integrates established theories — attachment theory, social exchange theory, and epistemic trust frameworks — and proposes a research agenda that bridges foundational psychology with emerging sociotechnical contexts. Rather than offering a deterministic view, MIRA provides a generative, testable structure for investigating the evolving role of AI in relational life and guiding future human–AI connection research.

The past twenty-to-twenty-five years of deception scholarship have seen a transformation in research foci and methodological approaches. Few attempts have been made to systematically organize this time-period in the verbal deception field and therefore, the current work takes up this opportunity to understand what scholars have been attending to and the scholarship that has made the most impact. To achieve these aims, the current chapter collected thousands of academic works and used topic modeling approaches to distill the literature to its most dominant themes. An overarching goal of this research is to gain a more generalized understanding of the verbal deception field, to evaluate the trends that go in and out of style, and to measure those that tend to have the most scholarly impact. Such patterns will tell scholars where the field has been and where it is headed in the future. To this end, the chapter begins with a primer on verbal deception research, followed by the explication of major themes in this field that have characterized the past two decades of scholarship.

Having too many online dating options has been theorized to hamper relational pursuit by making people choosier and ponder their options. However, daters do not make a choice just by comparing the value of their mates - they also consider how they will be evaluated by these mates and adapt behaviors accordingly. Therefore, being exposed to a larger option set may instead enhance relational pursuit outcomes by facilitating a compatible match and altering perception of one's own value in the market. We examined these possibilities in two preregistered experiments (NS1 = 193; NS2 = 342) where people chose a match from thirty-one (high-option) or six profiles (low-option) to go on a date with. High-option participants reported greater relational pursuit with their choice than low-option participants; match compatibility or similarity mediated this effect. The effect of option quantity appears less certain than once considered and warrants future investigation from diverse theoretical perspectives.(sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (NS1 = 193; NS2 = 342) .(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) ((sic)(sic)(sic)(sic)(sic)) (sic)(sic)(sic) ((sic)(sic)(sic)(sic)(sic)) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic); (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

The current paper draws on self-presentation theory and warranting theory to evaluate how the language patterns in an online marketplace reflect seller status (i.e., a prolific seller versus an everyday seller). Using 1.6 million musical instrument listings from Reverb.com in search of content, style, and structural differences in seller product descriptions, the evidence suggested prolific sellers tend to focus more on objective and functional aspects of a product (e.g., its features and specifications) and less on subjective characteristics like tone, relative to everyday sellers. Prolific sellers also communicated in a more narrative-like style, which was driven by an elevated use of personal pronouns, and they used longer descriptions than everyday sellers. Therefore, what prolific sellers focus on tends to be quite technical, but how they communicate this information is typical of a story that is told to potential buyers. Implications for self-presentation theory and warranting theory are discussed.

Conversation is ubiquitous in social life, but the empirical study of this interactive process has been thwarted by tools that are insufficiently modular and unadaptive to researcher needs. To relieve many constraints in conversation research, the current tutorial presents an overview and introduction to a new tool, Dyadic (https://www.chatdyadic.com/), a web-based platform for studying human-human and human-AI conversations using text-based or voice-based chats. Dyadic is distinct from other platforms by offering studies with multiple modalities, AI suggestions (e.g., in human-human studies, AI can suggest responses to a participant), live monitoring (e.g., researchers can evaluate, in real time, chats between communicators), and survey deployment (e.g., Likert-type scales, feeling thermometers, and open-ended text boxes can be sent to humans for in situ evaluations of the interaction), among other consequential features. No coding is required to operate Dyadic directly, and integrations with existing survey platforms are offered.

OBJECTIVES:We aimed to replicate the idea that physicians' self-presentation characteristics (i.e. formal vs. informal attire) and gender (i.e. men vs. women) influence perceptions of their warmth and competence. Further, we aimed to extend this line of work by examining how these relationships are moderated by trust in physicians. DESIGN:We conducted a 2 (physician gender: men vs. women) × 2 (self-presentation: formal vs. informal attire) experiment using publicly available physician images. METHODS:Students (N = 734) were randomly assigned to rate five physician images from a pool of 20 stimuli across physician gender and attire conditions. Participants rated physicians on warmth and competence, and then completed a trust in physicians scale. Linear mixed models evaluated main effects and interaction effects for self-presentation, gender and physician trust. RESULTS:Physicians in formal attire (white lab coats) were perceived as warmer and more competent than those in informal attire (casual or informal wear). Women physicians were rated as warmer, but not more competent than men physicians. Two-way interaction effects revealed formal attire enhanced perceptions of men physicians more than women on average. Three-way interaction effects indicated trust in physicians moderated these results, with women physicians' ratings being more dependent on participants' general trust levels, particularly for those with lower trust in the medical profession. CONCLUSIONS:We replicated and extended this literature by demonstrating how physician gender and patient trust levels moderate self-presentation effects. For physicians, understanding a patient's relationship with the medical establishment may help to inform their self-presentation choices.

In this paper, we examined five decades of language and persuasion research from an expert-curated list of seminal works in the field. We evaluated the most dominant themes in this literature (N = 1,886 papers), searching for time-related (e.g., themes that go in and out of style), impact-related (e.g., themes that associate with citation rates), and authorship-related trends (e.g., interdisciplinarity). The most prevalent themes were framing (64.5% of papers), content analysis (51.9%), and social media (50.1%) in language and persuasion research. Our computational literature review identified three additional findings: (1) the field has maintained relatively stable theoretical coherence, (2) papers that focus more on content analysis research receive fewer citations, and (3) the application of certain theoretical principles (e.g., argument quality) has declined over time, with researchers potentially citing underlying theories ritualistically versus meaningfully. Implications for language and persuasion research are discussed.

Human deception detection can be both poor in an absolute sense and, statistically, substantially better than chance. This empirical paradox is documented and explored in the present article by comparing raw percentages and signal detection metrics in a reanalysis of fourteen prior deception detection experiments (total N = 2,349 respondents; 32,776 truth-lie judgments from 5 different countries). We show that different analytic approaches applied to the same data can yield inconsistent or mixed findings. Measures of raw percent-correct accuracy and sensitivity are nearly perfectly correlated yet are open to very different descriptive interpretations. In contrast, raw and signal detection estimates of bias diverge, indicating competing interpretations of human judgment error. We advocate for avoiding simple face-value interpretations of both approaches and embracing multiple analytic approaches simultaneously. A new R package, liaR, is presented to facilitate raw and signal detection calculations in parallel.

Culture, a pivotal concept in social and behavioral science, elicits diverse interpretations. Insufficient recognition of this variance can hinder constructive dialogue across disciplines. This study aims to understand themes encoded in more than seven hundred definitions of culture collected from across disciplines. Two topic modeling approaches are compared - a novel use of congruence coefficient analysis in the LIWC based Meaning extraction method and Latent Dirichlet Allocation. The former approach was found to be more effective for uncovering themes in definitions of culture as text and resulted in a 16-factor varimax rotated model. In the latter approach a partially coherent 32-factor model was retained. The differences in the methods and results, and the applications of the 16-factor structure as a marker for identifying cultural relevance in texts are discussed.

Despite widespread discussions about Artificial Intelligence (AI) and its impact on society, little work has objectively measured how often people use this technology in the wild. The present paper collected up to 90 days of web-browsing data from students (Study 1: N = 499) and those in the general public (Study 2: N = 455), quantifying how often people used AI and evaluating the psychological correlates of such use. Upon coding 4.1 million websites in Study 1 and 9.9 million websites in Study 2, the evidence suggested AI use was relatively infrequent, totaling 1% of student web-browsing and 0.44% of general public web browsing, on average. The most consistent predictors of AI use across studies were aversive personality traits (e.g., Machiavellianism, narcissism, psychopathy), albeit the traits were differentially associated with AI use across studies. Demographics were systematically unrelated to AI use across studies. Finally, we observed that self-reported AI use and actual AI use were only moderately correlated (ρ = .329), suggesting limitations in subjective measures of media use. These findings provide some of the first behavioral measurements of AI in naturalistic settings and establish important benchmarks for understanding the individual differences associated with AI adoption.

People often rely on numeric information to make better decisions. But are numbers always used in a deliberative manner? In preregistered studies, we demonstrated that the presence vs. absence of at least one Arabic integer in A/B tests of headlines increased decisions to click-through from a headline to an article by 3.6% for the Washington Post (7,371 experiments, 19,926 headlines) and 5.2% for Upworthy (22,664 experiments and 105,551 headlines) after controlling for other factors known to increase such engagement. A preregistered within- and between-participant experiment (N=765) further revealed that the presence of at least one Arabic integer (“3”)—relative to verbal labels (“several”) or written-out numbers (“three”)—increased such selections, positive impressions of headlines and their writers, and recall. These findings suggest that people perceive stories associated with Arabic-integer headlines as more valuable and deserving of their limited attention. The effects are inconsistent with Arabic integers attracting more attention and precise numbers providing more information because 1) when precise numbers were written out, they did not have the same effects and 2) using fonts to draw attention to written-out numbers and verbal terms did not increase selections of those headlines over Arabic-integer headlines. Instead, people seem to use a number heuristic—with Arabic integers providing more valued information over other number forms—that superficially guides people’s memory, message perceptions, and engagement with messages. News organizations and other communicators should think more about when and how to harness the power of numbers.

This paper evaluates how AI-Mediated Communication (AIMC) influences impression formation. By drawing on and extending warranting theory, this paper examined how source roles (self vs. third party) and writer labels (AI vs. human) interact to affect impression formation and message effects in the AIMC setting of hotel reviews. Two pre-registered experiments (Study 1: N = 461; Study 2: N = 444) found that, for the same hotel review, third-party reviewers were perceived more favorably than self-reviewers (i.e., hotel managers), leading to more positive evaluations of the hotel. Reviews attributed to AI writers negatively impacted impressions of the reviewer and diminished the review’s impact, with third-party reviewers more negatively affected by AI writer labels than self-reviewers. AI labels of user-generated content can therefore move people out of their default impression formation state. We demonstrate theoretical advancements for warranting theory and AIMC.

Deception research has traditionally evaluated how individual differences like personality traits and demographics correlate with lying. However, the establishment of adverse childhood experiences (ACEs) as an individual difference that also links to deception remains underexplored. To this end, the present study (N = 784 students) investigated the relationship between ACEs and deception in adulthood. Results indicated that individuals with more (versus less) adverse childhood experiences, particularly those involving maltreatment and victimization, reported more daily white and big lies, independent of aversive personality traits like narcissism and Machiavellianism. Consistent with other studies on individual differences and deception, the effect sizes were small, but systematic. Together, these findings support the dispositional honesty hypothesis, indicating that foundational childhood experiences and events can shape or signal deceptive behavior. Generally, the study contributes to our underexamined knowledge base of the developmental antecedents of lying, emphasizing the role that adversity plays during childhood to influence deceptive behavior beyond commonly studied personality traits.

This paper examines the critical, yet undertheorized role of context in verbal communication when computers are used to analyze language data. Drawing on multiple disciplines and prior work from communication and social psychology, an interactionist perspective is reviewed and applied to computational settings to demonstrate how individual, interpersonal, social, and situational factors afford or constrain verbal behavior. Using examples of language complexity and processing fluency research, this paper demonstrates how contextual factors like instrumental goal activation can reverse seemingly established relationships between simple language features and behavioral outcomes. The paper concludes by advocating for methodological approaches that systematically integrate contextual variables into natural language processing research, enabling scholars to resolve contradictory findings, enhance replicability, and develop a more nuanced understanding of how language reveals psychological processes.