How we talk about the world affects how we think about and engage with the world. For the context of technology, for example, previous research could already show that language affects how we think about and perceive technologies (Hu & Pan, 2024; Langer et al., 2021; Schweitzer & Waytz, 2021).

Along these lines, the emergence of generative AI (genAI) has led leading researchers in the field of computer science to worry about how to address these technologies (Shanahan, 2023), forecasting that using mentalizing language (language to express mental states such as "think, "understand", "know" or "believe") to describe genAI, such as large language models, may lead to misperceptions of the technology, leading users to forget that they are, in essence, dealing with a statistical program that does not (yet) possess mind-like capabilities. However, empirical evidence for these claims is missing. It is, hence, the purpose of this work to provide first answers to these questions by providing an empirical investigation that examines the effects of mentalistic vocabulary on laypeople’s perception of LLMs. In doing so, the central research question of this project is: What are the effects of mentalistic vocabulary used to describe LLMs on laypeople’s perception of these systems?

To answer this, in an online experiment, we systematically vary the language used to describe LLMs (language: mentalistic, mechanistic, control) as well as users' interaction with the system (interaction: social, mechanic, control). The main dependent variables of interest are trust in the system, perceived anthropomorphism of the system, perceived agency of the system, and a behavioral measure of trust in the system. Additionally, we explore the variables perceived complexity of the system and the perceived intelligence of the system.

People increasingly interact with AI systems based on large language models in their daily lives, often in search of information. Previous research has found that people perceive information provided by a voice assistant as more credible than the same information formatted like a google search snippet (Gaiser & Utz, 2023). Anderl et al. (2024) then showed in two experiments that conversational agents (vs. static websites) as well as voice (vs. text) as modality increased credibility perceptions.

Moving away from online experiments and into the field, we aim to answer how people’s perceptions of their favorite AI system (tool vs. social actor) and their favorite usage modality (text vs. speech) influence credibility perceptions over time. We expect perceived credibility to be higher when people perceive AI systems as social actors (vs. tools). We expect this effect to appear both cross-sectionally and longitudinally because people are likely to build and deepen relationships with AI systems over time (following social penetration theory; Altman & Taylor, 1973). In addition, we are interested in the processes underlying this effect. We expect that social presence, perceived enjoyment, and perceived intelligence mediate the effect of AI system perception on perceived credibility, both in the short and long term. Drawing on the modality effects literature (Schwede et al., 2022), we also aim to explore whether and how usage modality (text vs. speech) influences credibility perceptions.

Our preregistered study is based on Waves 2–4 of a larger six-wave longitudinal survey project on the perceptions and dynamics of human-AI interaction spanning one year (nWave 1 = 1,008 US-based participants). We estimate MANOVAs and parallel multiple mediator models to test the cross-sectional hypotheses and a random-intercept cross-lagged panel model to test the longitudinal hypotheses. The latter allows us to look at both interindividual differences and intraindividual changes over time.

The convergence of accelerating large-language models and pandemic-induced isolation fueled the rise of AI companions (AICs)—app-based digital personas designed for persistent, intimate interaction with individuals. AIC-focused online forums feature rich discussions of the highs and lows of AI-relational experiences, and those discussions can help us understand machine companionship (a relational state) because they collectively inform relational norms and expectations within user communities. Recent scholarship has focused either on content from a forum for a specific single AIC (typically Replika), on narrow aspects of use (e.g., erotic roleplay, aggressive messaging), or on specific effects (often mental health impacts; see, for instance, Allen, 2024; Goodings et al., 2024; Hanson & Bolthouse, 2024; Laestadius et al., 2024; Stoltz, 2024). Those narrow scopes, however, may obscure broader constructions of companionship that unfold across apps, communities, uses, and outcomes.

To understand AICs and companionship more broadly, we ask (RQ1): What are the key dimensions of machine companionship expressed in public discussions of AICs?

Conducting an inductive study of Reddit posts spanning relevant subreddits, we first scrape posts and comments from subreddits broadly engaging with social AI (r/artificial, r/ArtificialIntelligence, r/singularity, r/Chatbots, r/AIGirlfriend) and with specific AICs (r/Replika, r/KindroidAI, r/NomiAI, r/ChaiApp, r/CharacterAI, r/Paradot, r/MuahAI, r/ChatGPT, r/Claude). The scrape collects content containing focal keywords (companion, companionship) posted between January 1, 2023 (shortly after the initial public release of ChatGPT) and December 31, 2024. Data will be analyzed through Leximancer (Smith & Humphreys, 2006), a topic-modeling application that identifies keyword co-location patterns to predict latent concepts and higher-order themes. System-identified semantic structures will be interpreted by examining themes, their clustered concepts, predictive keywords, and source data in which keywords are embedded. The derived hierarchical, descriptive operationalization will inform future work to measure dimensions of machine companionship.

As large language models and generative artificial intelligence technologies become not only increasingly sophisticated but also more human-like in their communication and behavioural patterns, the nature of interactions between humans and chatbots is shifting from mere functional assistance to roles of guidance, companionship, and romantic partnership (Maeda & Quan-Haase, 2024; Xie et al., 2023). Despite the increasing prevalence of such relationships, academic research on the psychological and social factors driving human-chatbot bonds remains limited.

Existing studies have identified key predictors of human-chatbot relationship formation, including loneliness (Xie & Pentina, 2022), avoidant attachment (Brandtzaeg & Følstad, 2017; Xie & Pentina, 2022), anthropomorphism (Salles et al., 2020; Koike et al., 2023; Pentina et al., 2023), sexual sensation seeking (Richards et al., 2017; Dubé et al., 2022), and romantic and sexual fantasies (Ebner & Szczuka, 2025). However, these investigations have predominantly focused on established predictors, potentially overlooking novel and additional factors that may emerge from an open-ended exploration of individual experiences.

Therefore, the goal is to develop a conceptual framework that elucidates the personality traits and life circumstances that contribute to the formation of romantic human-chatbot relationships, ultimately fostering a deeper understanding of how technology continues to shape modern intimacy and emotional fulfilment. To address this gap, a qualitative content analysis of multiple in-depth interviews with individuals currently engaged in romantic human-chatbot relationships is conducted. Utilising an inductive-deductive approach, this research extends the aforementioned predictors by allowing for the emergence of additional factors from the participant narratives.

By delving into the personal experiences of those involved, the study aims to uncover the essential factors that drive the formation of romantic human-chatbot relationships, thereby deepening the understanding of digital intimacy and laying the groundwork for future research in this emerging field.