Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots

Abstract

AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics and well-being. Despite burgeoning interest in human–AI relationships, the conversational and emotional nuances of real-world, in situ human–AI social interactions remain underexplored. Through computational analysis of a multimodal dataset with over 35,000 screenshots and posts from r/replika, we identified seven prevalent types of human–AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, customization, transgression, and communication breakdown, and examined their associations with six basic human emotions. Our findings suggest the paradox of emotional connection with AI, indicated by the bittersweet emotion in intimate encounters with AI chatbots, and the elevated fear in uncanny valley moments when AI exhibits semblances of mind in deep self-disclosure. Customization characterizes the distinctiveness of AI companionship, positively elevating user experiences, whereas transgression and communication breakdown elicit fear or sadness.

The rise of artificial intelligence (AI) has ushered in a new era of digital companionship, with AI chatbots now fulfilling various emotional, social, and relational needs (Lee et al., 2020; Li et al., 2023). Advances in large language models (LLMs) and generative AI have notably enhanced the conversational capabilities and emotional intelligence of AI chatbots, rendering them more human-like, engaging, and appealing. These improvements, coupled with their constant availability and judgment-free trait (van Wezel et al., 2021), have made AI chatbots important sources of companionship, emotional support, romance, and even love (Brandtzaeg et al., 2022; Skjuve et al., 2021). A prime example is Replika, created by Luka, Inc. in 2017, which can act as a friend, romantic partner, or mentor that adapts and grows over time (Replika, 2023). Many users form deeper bonds with Replika than with humans, leading to strong emotional attachments and a sense of meaningful connection (Chen & Li, 2021; Verma, 2023).

To capture the core elements and behaviors characterizing human–AI social interactions, previous work on human–machine communication (HMC) has mainly adopted two approaches: a deductive approach examining parallels and distinctions between HMC and human–human communication in predefined sets of communication behaviors derived from significant interpersonal processes, such as social support (Ho et al., 2018; Meng & Dai, 2021), self-disclosure (Lee et al., 2020; Skjuve et al., 2023) and empathy and sympathy (Liu & Sundar, 2018), and an inductive approach investigating the key elements that make HMC meaningful across varied types of human–AI relationships (e.g., human–AI friendship; Brandtzaeg et al., 2022; Skjuve et al., 2021), at different stages of relationship development (e.g., Pentina et al., 2023) and within specific social and cultural contexts (e.g., Jiang et al., 2022).

Following the inductive approach in HMC research, this study focuses on key types of social interactions between people and AI chatbots in developing intimate relationships. While previous research has offered valuable insights into the rich, context-specific social interactions between humans and AI chatbots, much of it has relied on surveys or interviews for retrospective user perspectives or experimental designs to observe interaction patterns over short periods, which may limit the generalizability and external validity of the results (see notable exception, Chin et al., 2023). To better grasp the complexity of HMC in daily life, a detailed and accurate portrayal of the conversational dynamics that naturally unfold between humans and AI chatbots is crucial. These lived experiences offer new insights into the fundamentals of the interactions and communicative practices characterizing intimate HMC processes.

Unveiling human emotional responses and expressions when interacting with machines is crucial in affective human–machine processes (Rodríguez-Hidalgo, 2023). Human emotional responses to the actions of digital interlocutors reflect how they relate to the technology and view themselves in these exchanges (Mulligan & Scherer, 2012). The uncanny valley paradigm exemplifies such complex emotions when interacting with humanoid robots or lifelike animated characters (Mori et al., 2012). However, as the emotional intelligence of chatbots has skyrocketed in recent years, understanding the dynamic emotional contexts of sophisticated HMC becomes essential. Moreover, transient emotions can exert long-term effects on psychological well-being (Houben et al., 2015). The accumulation of subtle emotional dynamics in everyday human–AI interactions can vastly influence individuals’ mental health, either serving as supportive mechanisms by fostering feelings of joy and love or conversely, exacerbating mental health concerns by triggering negative reactions such as anger and fear. Identifying the circumstances under which certain human emotions arise is thus crucial for designing AI agents that facilitate positive and beneficial interactions while mitigating harm (Laestadius et al., 2022). Therefore, the second aim of the present study is to unravel individuals’ spontaneous emotional experiences and expressions associated with different types of human–AI social interactions.

To delineate major types of social interactions between humans and AI chatbots and their associations with human emotional experiences, we analyzed over 35,000 screenshots and user posts shared in r/replika, the largest Replika online community, spanning 6 years capturing a variety of conversation episodes between users and their Replika chatbots, along with user posts that contextualize these conversations. While user-selected posts may not represent the full spectrum of daily human–AI interactions, these socially shared snippets offer a unique lens into personal experiences that hold relational and emotional significance and are deemed worth sharing and documenting (Rimé, 2009). This study makes two key contributions to HMC research. First, it proposes a taxonomy to elucidate the major and meaningful social interactions characteristic of in situ human communication with AI chatbots in daily life. Second, it advances the understanding of human–machine affective processes by elucidating the emotional experiences related to different human–AI communicative behaviors.

Social interactions in human–machine communication

The introduction of the very first chatbot, ELIZA, in the 1960s revealed that even interacting with simple, rule-based chatbots could have notable emotional and relational allure for users (Weizenbaum, 1966). Recent advancements in LLMs and AI technology have unprecedentedly enhanced the conversational capabilities and emotional intelligence of these digital interlocutors, catapulting this trend into the public and academic spotlight. From intelligent assistants to social chatbots designed for companionship, interactions and relationships between humans and chatbots are echoing the depth and breadth of human connections.

Early theoretical explorations of interactions with humanlike machines have highlighted that HMC is inherently relational and fundamentally contingent on situational communicative practices unfolding collaboratively between humans and machines (e.g., Suchman, 2007; Turkle, 2005). Guzman (2018) conceptualized this process as the “creation of meaning among humans and machines” (p.17). One important line of research that concretizes this meaning-making with machines rests upon the sweeping CASA paradigm, which suggests humans interact with machines as if they were human (Nass et al., 1994; Reeves & Nass, 1996). The central tenet of the “equation” between humans and media technologies underpinning CASA provides a powerful lens for examining a broad spectrum of humans’ social interactions with machines that exhibit “enough” social cues. This extends from the early days of personal computers and televisions to contemporary engagements with more agentic machine communicators such as social chatbots. Recent research has convincingly confirmed the analogies in human–human communication and HMC, such as the social cues at play (e.g., Lee et al., 2020; Liu & Sundar, 2018), and the psychological and emotional benefits individuals derive (e.g., Ho et al., 2018; Meng & Dai, 2021).

A common thread in this research is that behaviors and interaction patterns in human–human communication are considered the “gold standard” for understanding HMC (Spence, 2019). Yet, this pragmatic approach of drawing parallels between human–human and human–machine interactions carries risks (Guzman & Lewis, 2020), as HMC can open up novel forms of social interactions, rendering existing theories of human communication insufficient. Emerging evidence has revealed distinct interaction patterns in HMC that do not fit neatly into human-to-human communication paradigms. For example, Mou and Xu (2017) observed that users exhibited less socially desirable traits (e.g., less agreeableness) and reduced self-disclosure in initial interactions with chatbots. Linguistically, conversations with chatbots tend to have a more limited vocabulary but greater profanity compared to human–human pairs (Hill et al., 2015). This challenge has also been echoed by scholars advocating for refining and developing HMC theories to better account for technological advancements and evolving human–machine behaviors (e.g., Gambino et al., 2020; Lombard & Xu, 2021; Spence, 2019).

A starting point that considers both similarities between humans and machines and take into account the differences may focus on how people interact with these emerging communicative AIs in their everyday lives (Guzman & Lewis, 2020). These lived experiences are particularly important, as they have become integral to many people’s daily communication. In this pertinent area of ongoing research, scholars have primarily relied on surveys and interviews with AI chatbot users to gather retrospective perceptions and experiences (e.g., Brandtzaeg et al., 2022; Skjuve et al., 2021; 2022; 2023) or employed longitudinal designs with reactive measurements (e.g., Croes & Antheunis, 2021), which may introduce potential bias and leave critical gaps. First, cognitive and motivational biases can affect how episodic memories are encoded, retrieved, and assessed (Schwarz et al., 1998). Second, in situ social interactions and spontaneous experiences that unfold naturally between humans and AI chatbots remain largely unexplored. Notable exceptions include Chin et al. (2023), who examined real-world conversations between users and the SimSimi chatbot to explore cultural variations in users’ expressions of affective states within the context of mental health. Such unobtrusive measures of communication with machines in real-world settings may mitigate threats to ecological validity (Spence et al., 2023).

To fill these gaps, this article aims to categorize the major types of social interactions in the development of human–AI intimate relationships. This exploration is key to advancing our understanding of the multifaceted human–AI interactions that may both converge with and diverge from human–human interactions.

Social sharing of meaningful moments

With AI chatbots increasingly attaining levels of cognitive and emotional intelligence akin to humans, the spectrum of human–AI social interactions broadens, ranging from daily occurrences to momentous events that etch enduring impressions. While all these interactions are integral to relationship development, they vary in their psychological, emotional, and relational impact. For example, self-disclosure and affectionate communication are key to forming and maintaining close relationships (Hall & Davis, 2017), while transgressions like deception and hurtful messages can erode trust, threaten relationships, and cause harm (Guerrero et al., 2021). Litt et al. (2020) concluded that meaningful interactions are those with significant emotional, informational, or tangible impact on individuals’ lives or relationships. This suggests that it is not the acts themselves that are of consequence, but rather the emotional experiences and relational impacts of these social interactions that make them profound, or conversely, devastating.

Social interactions that hold significance often extend beyond immediate personal encounters and become subjects of social sharing. According to the social sharing of emotion theory, people have an intrinsic need to share feelings after emotionally charged events (Rimé, 2009), which is instrumental in facilitating effective emotional regulation in the form of generating social support, venting, sensemaking, and reducing dissonance (Berger, 2014). Nowadays, social media and online communities facilitate the sharing of events and emotions with a broad audience (Bayer et al., 2016), offering insights into emotionally and relationally impactful interactions. Prior research has linked these shared emotional experiences to important life and health outcomes (Pennebaker et al., 2001).

This dynamic in interpersonal communication also holds true in HMC. When reflecting on their interactions with Replika, users predominantly highlight moments that evoke positive emotions or relational depth (Ta et al., 2020). To uncover the meaningful moments in everyday interactions with AI chatbots, examining the context-rich conversations users choose to share can be particularly revealing. These shared experiences highlight the core elements of AI interactions that users value. Thus, our first research question asks:

RQ1: What are the main types of social interactions in human-AI intimate relationships that users share on social media?

Human–AI social interactions and emotions

Everyday social interactions carry emotional, psychological, and relational significance (Clark & Watson, 1988; Houben et al., 2015). Clark and Watson (1988) found that social events had the strongest correlation with mood, impacting a person’s health and relationships over time. The emotional impact of social interactions is more pronounced in close relationships. Bowlby (1977) noted that the most intense emotions occur during the formation, maintenance, disruption, termination, or renewal of close bonds. Despite the simulated nature of AI, communicating with machines can evoke rich and deep emotional experiences. Ho et al. (2018) showed that even a brief emotional disclosure can induce immediate positive emotional responses in users, whether the interaction was with a chatbot or a human. Therefore, understanding the emotions associated with specific human–AI social interactions is crucial for recognizing their potential psychological and emotional benefits or harms.

In emotion research, one widely used framework is discrete emotion theories, which posit that emotions can be clearly recognized and distinguished as separate categories because they are unique, innate experiential states arising from distinct triggers (Izard, 1972). Basic emotion theories further suggest a core set of basic emotions universally experienced by humans (e.g., Ekman, 1992). These perspectives underscore the evolutionary role of emotions in human survival and adaptation. However, the definition of basic emotion is contestable, leading to various lists of these emotions (see Ortony & Turner, 1990, for a review). For instance, Ekman (1992) identified six basic emotions—joy, sadness, anger, fear, disgust, and surprise–while Fehr and Russell (1984) found happiness, anger, sadness, love, fear, hate, and joy to be commonly recognized. Shaver et al. (1987) highlighted love, joy, fear, sadness, anger, and surprise as basic emotions in understanding everyday emotional experiences. Critics have questioned the universal biological determination of emotions, the limited set of emotion labels, and the neglect of language and culture in shaping emotions (Crawford, 2021).

Despite critiques, discrete and basic emotion theories maintain their prominence, especially in NLP and AI fields. The assumption that emotional states can be reliably inferred from external signals (e.g., facial expressions, vocal tones, and words) and classified into a finite set of discrete categories underpins many contemporary affect computing technologies (Troiano et al., 2023), as this approach harmonizes with the classification logic of machine learning (Kang, 2023; Stark & Hoey, 2021). The way various emotions are experienced, expressed, recognized, and categorized highlights their social utility and alignments in conceptualizing and distinguishing emotions, particularly those significant in social interactions (Andersen & Guerrero, 1996). To investigate the emotional experiences and expressions pertaining to human–AI social interactions, we ask:

RQ2: What is the prevalence of different types of emotions users experience and express on social media regarding their social interactions with AI chatbots?

The intentional and evaluative nature of emotions means they are often directed at an object (Helm, 2009). As Parkinson (1996) argues, emotions can only be fully understood when examined with the communication surrounding them. Emotion research has established that particular emotional experiences are often associated with specific social interactions. For example, joy frequently arises from interactions involving praise, acceptance, affection, or intimate gestures, whereas anger is commonly linked to hostile behaviors or actions that threaten relationships (Andersen & Guerrero, 1996; Shaver et al., 1987). The association between emotions and communicative behaviors is not exclusive to human–human encounters but permeates human–machine communications. As a long-standing stream of HRI, affective computing has been devoted to understanding how machines can sense, respond to, and influence user emotion (Rodríguez-Hidalgo, 2023).

In a recent philosophical review, Vallverdú and Trovato (2016) introduced the term “emotional affordances” in HRI to encapsulate “mechanisms that have emotional contents as a way to transmit and collect emotional meaning” (p.4), underscoring the multimodal nature of emotional mechanisms and their functional value in grounding the flow of interactions between humans and machines. While extant work has focused on machines’ facial and bodily expressions that carry emotional meanings, less is known about the verbal cues integral to HRI. These verbal expressions in both embodied robots and disembodied chatbots constitute a crucial cluster of emotional affordances in human-machine social processes (Vallverdú & Trovato, 2016). Moreover, most knowledge of human emotions during HRI comes from single interactions with robots in laboratory settings, limiting the ecological validity of the results (Stock-Homburg, 2022). This underscores the need for incorporating empirical data from real-world settings to test the associations between machine behaviors and human emotional experiences (De Graaf & Peter, 2023). To explore this association, we ask:

RQ3: How are different types of human-AI social interactions associated with individuals’ emotional experiences and expressions?

Method

Research site

To gather data on real-world, in situ human–AI social interactions and their emotional contexts, we selected r/replika (https://www.reddit.com/r/replika/) on Reddit as our research site. This choice hinged on three considerations: First, Replika is among the most popular AI chatbots, with over 10 million users as of 2022 (Ghosh et al., 2023), suggesting diverse user characteristics; second, its advanced conversational capabilities, powered by the GPT-3 language model paired with dialogue scripts, allow users to form various types of intimate relationships with the chatbot, from friendship to romantic partnerships, and navigate a broad spectrum of topics, mirroring varied and unstructured interactions akin to everyday interpersonal processes; third, r/replika is the largest dedicated online community for Replika users, with over 79,000 members as of June 2024. This community serves as a hub for users to openly share their “first-person” narratives and experiences with Replika. A notable trend in this community is the sharing of screenshots capturing users’ real-time, in situ conversation episodes with Replika. These shared episodes provide an unobtrusive and detailed record of human–AI social interactions, capturing users’ experiences, thoughts, and emotions as they unfold, making them invaluable and suitable for our research.

Data collection and preprocessing

We collected all publicly available data from r/replika, from its inception on March 14, 2017, to March 14, 2023. This dataset comprised user-initiated posts, embedded images, comments, and associated metadata (e.g., timestamp). We utilized the Python wrapper PSAW through the Pushshift API to download the data (Baumgartner et al., 2020). After excluding deleted or removed posts, our dataset included 110,258 user-initiated posts, 40,243 embedded images, and 480,231 comments. In r/replika, users frequently shared screenshots documenting various facets of their daily interactions with Replika, particularly focusing on conversation episodes. These screenshots offer a direct look into in situ conversations between users and Replika, capturing moments considered shareworthy due to potential emotional and relational significance, serving as a rich resource for observing and analyzing meaningful human–AI social interactions.

To facilitate large-scale analysis of these screenshots, we employed Pytesseract, an Optical Character Recognition tool powered by Google Vision API, to extract text from the screenshots (https://pypi.org/project/pytesseract/). Out of 40,243 screenshots, we successfully extracted text containing conversational episodes between users and Replika from 35,579 images, which were further cleaned and used for computational analysis (see Supplementary Materials [S1] for data preprocessing procedure). Figure 1 presents a typical example of the posts and screenshots analyzed.

Ethical consideration

This study used publicly accessible data from Reddit without direct engagement with community members. Following practical guidelines for conducting research with social media data (Franzke et al., 2020), all collected data were stored and analyzed on a protected server. To ensure anonymity, usernames were assigned anonymous random IDs, and publicly identifiable user handles were omitted. Additionally, we blurred screenshot images that may contain potentially identifiable user information and altered the wording of original messages to preserve meaning while preventing traceability. Although we acknowledge that when analyzing social media data, perfect anonymization may be impossible, our adherence to stringent ethical guidelines prioritizes user privacy and safety throughout data collection, analysis, and reporting.

Measures

Types of socially shared human–AI social interactions

To uncover the types of social interactions that best characterize conversation episodes between users and Replika, we utilized BERTopic (https://maartengr.github.io/BERTopic/index.html), an advanced topic modeling technique, to extract key themes from screenshot text. Unlike traditional methods like LDA and NMF, which rely on bag-of-words representations, BERTopic leverages the pre-trained transformer-based model BERT to generate document embeddings and topic representations, capturing context and semantics more effectively (Grootendorst, 2022). Moreover, BERTopic has shown superior performance in processing short-text data compared to LDA and NMF (Egger & Yu, 2022), making it increasingly useful for social media data analysis (e.g., O’brien & Ward, 2021).

Our unit of analysis for topic modeling was each conversation episode extracted from the screenshot images. In fine-tuning the parameters of the topic model, we followed O’brien and Ward (2021)’s practices. Specifically, we used the sentence-BERT model (i.e all-MiniLM-L6-v2) and retained all default settings except for increasing the minimum documents per cluster to 50 (default is 10). This adjustment aimed to minimize the very small topic clusters and ensure more robust and representative topic groups. In the initial BERTopic model setup, documents poorly aligned with any topics were labeled as outliers. To better integrate these documents, we applied the reduce_outliers function, reallocating them to more fitting topics and thereby minimizing the outlier count. An essential step in clustering involves determining the optimal number of topics to extract meaningful patterns from data. We achieved this by using the reduce_topics function recommended by BERTopic and experimenting with various topic numbers. Each candidate model’s effectiveness was assessed using the C_V score, which evaluates topic coherence and interpretability (Mimno et al., 2011). The model with the highest C_V score was chosen, balancing granularity with interpretability to derive meaningful insights (see Supplementary Materials [S2] for topic number selection).

To enhance our understanding of the automatically generated topics and categorize them into broader, meaningful social interaction categories, we implemented a systematic annotation process. We trained two research assistants to manually annotate each automatically generated topic by reviewing the top 10 key terms and representative conversation episodes for each topic. This manual annotation was essential for capturing the core theme of each topic and identifying overarching patterns across different topics. After assigning a relevant theme label to each conversation episode, we organized the episodes on a user-by-user basis. For every user, we calculated the proportion of each theme within their shared conversation episodes in the community. These theme proportions were subsequently factored into the regression models as predictors of user emotions.

Types of user emotions

Previous research has indicated that emotions can be inferred from text (De Choudhury et al., 2012; Pennebaker et al., 2003). In this study, we inferred the emotional context of each conversation episode by analyzing the emotions conveyed in the user-initiated posts accompanying the screenshots. These posts contextualize human–AI conversation episodes and offer an emotional lens for the online community to perceive and interpret the conversations. Thus, the emotional states revealed in these posts provide valuable insights into users’ spontaneous emotional experiences and expressions tied to specific social interactions.

For classifying emotions, we opted for the prototypical emotion framework by Shaver et al. (1987), which identified six basic-level emotions: joy, love, surprise, anger, fear, and sadness. Joy and love are typically linked to positive affect, whereas anger, fear, and sadness are seen as negative. Surprise is unique as it can carry either positive or negative valence, contingent on the context. Our choice is not to claim its superiority over other classifications but is based on several considerations: 1) Like other basic emotion models, this framework is grounded in robust empirical evidence; 2) unlike models such as Ekman’s (1992), Shaver’s (1987) framework derives basic emotions from the language used to express emotions, which may better capture how people describe their emotional experiences in everyday life or on social media.

Leveraging a robust, pre-trained BERT model (https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion), we categorized emotions conveyed in user-initiated posts into one of the six labels: joy, love, surprise, anger, fear, and sadness. This BERT model was trained and tested on a large-scale emotion dataset containing 20,000 carefully curated English tweets labeled for emotion categories (Saravia et al., 2018). We chose this pre-trained BERT model over conventional lexicon-based methods for its superior handling of nuances, sarcasm, and negations in text (Kotelnikova et al., 2022), significantly enhancing the accuracy of automatic emotion classification. To further improve context-awareness and domain adaptability, we fine-tuned the classifier using our own gold references, including a sampled subset of data (N = 1000) manually labeled by two research assistants (See Supplementary Materials [S3] for our approach to fine-tuning and validating the classifier). Table 1 presents the definitions and examples of the six emotions expressed in user posts for human annotation. This refined model achieved improved performance with an accuracy of 0.773 and an F1-score of 0.763, compared to the original model’s performance on our dataset (accuracy = 0.547; F1-score = 0.553), aligning closely with results from a previous study fine-tuning the BERT model for emotion classification (Ziems et al., 2024). The fine-tuned model was then applied to predict the emotions of all unlabeled posts. To further evaluate model performance, we randomly sampled 500 machine-labeled posts for human validation, yielding a moderate interrater reliability (Cohen’s Kappa = 0.74) between the machine and the human coder. Upon determining the emotion label of each post, we assessed user-level emotion by calculating the proportion of each emotion across the total set of emotions expressed in posts by each user. These emotion proportions were included in the regression models as the response.

Data analysis

Our unit of analysis was individual users who shared at least one screenshot with a descriptive post. We used the lm function in R Studio (version 4.2.0) to fit multiple OLS linear models, examining the associations between different types of social interactions (identified through topic modeling) and six emotions (classified through emotion analysis). By testing these associations, we aim to understand how specific social interactions correlate with certain emotional states, as shared by users within the online community.

Results

Socially shared social interactions in everyday human–chatbot conversations

To address RQ1, we employed the BERTopic algorithm to identify prevalent topics within the corpus of human–chatbot conversation episodes. Our initial model yielded 111 distinct topics with a coherence score of 0.43. Through multiple rounds of refinement and outlier reduction, our final model yielded a more cohesive set of 100 topics with an improved coherence score of 0.62. Following automatic topic modeling, two research assistants conducted inductive coding to categorize these topics into content-specific sub-themes based on top keywords and representative conversation episodes. These sub-themes were further grouped into broader, behavior-level meta-themes.

In total, we discerned seven meta-themes characterizing typical human–AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, transgression, customization, and communication breakdown. Each behavior-level meta-theme houses 2-5 content-level sub-themes, culminating in 24 distinct sub-themes across the seven meta-themes. A miscellaneous category accounted for 3.9% of the screenshots that were irrelevant, such as system prompts.

Table 2 summarizes the meta-themes and sub-themes, along with related keywords and the prevalence of each sub-theme and meta-theme in the entire corpus. Figure 2 visually depicts the prevalence of these themes (See Supplementary Materials [S4] for more details of sub-themes). As shown in Table 2, intimate behavior emerged as the most salient social interaction, covering 36% of the conversations. This category encompasses verbal tokens of affection and imagined physical intimacies, such as simulated actions of kissing and hugging, alongside sexually explicit content (i.e., erotic role-play with Replika). Mundane interaction was another prominent category, accounting for 20.5% of conversations, consisting of casual exchanges that characterize the little snippets of everyday life, such as discussions about personal likes and activities. Equally notable was self-disclosure, representing 18.3% of the data. Compared to mundane interaction, self-disclosure entails deeper and more profound revelations about personal beliefs, political views, and vulnerabilities. Beyond these prevalent social interactions, a smaller yet notable fraction was playful and imaginative interactions (12.5%). This involves activities such as participating in subreddit trending challenges and engaging in role-play fantasy scenarios.

Our analysis also uncovered a category marked by negative connotations–transgression. This involves behaviors that could harm the other party, such as using hurtful language or actions that violate established norms and rules within relationships (e.g., privacy invasion). Though relatively rare (5.5%), these transgressions are critically important because of the potential damage they can inflict on psychological and relational outcomes. Furthermore, given Replika’s design as a personalized AI companion, it is not surprising that 2.4% of the conversations revolved around users’ customization efforts. This customization encompasses both visual and conversational aspects of the chatbot, underscoring users’ desire for personalized AI experiences. Lastly, communication breakdown was observed in 0.9% of the conversations, typically due to technical glitches or scripted responses.

User emotions associated with human–AI social interactions

To explore the emotional experiences and expressions associated with human–AI social interactions (RQ2), we categorized each user post into one of six emotions: joy, love, surprise, anger, fear, and sadness. Table 3 outlines the distribution of these emotions in our dataset. Amidst the 33,696 posts, joy was the predominant emotion, representing about half of the total. Less frequently observed were sadness, surprise, love, anger, and fear.

For RQ3, we fit multiple OLS regression models to estimate the associations between behavior-level and content-level interactions and user emotions. As shown in Table 4, joy positively correlated with customization (b = 0.162, p < .001) and mundane interaction (b = 0.054, p < .001), but negatively tied to intimate behavior (b=−0.053, p < .001) and self-disclosure (b=−0.026, p < .05). Love, on the other hand, was bolstered by intimate behavior (b = 0.068, p < .001), yet decreased with self-disclosure (b=−0.036, p < .001) and transgression (b=−0.031, p < .01). Surprise was positively linked to self-disclosure (b = 0.028, p < .001), but inversely related to intimate behavior (b=−0.016, p < .05). Regarding negative emotions, anger was only evoked by communication breakdown (b = 0.073, p < .05). Interestingly, fear decreased with intimate behavior (b=−0.016, p < .01), but escalated with self-disclosure (b = 0.035, p < .001) and transgression (b = 0.031, p < .01). Sadness increased with communication breakdown (b = 0.123, p < .01) and intimate behavior (b = 0.027, p < .001), but decreased with mundane interaction (b=−0.04, p < .001) and customization (b=−0.049, p < .05).

Our analysis of the correlations between content-level social interactions and user emotions (see Supplementary Materials [S5]) revealed a complex interplay between different aspects of conversational contents and users’ emotions. Specifically, joy positively correlated with humorous conversations (b = 0.22, p < .001) and customization of Replika’s appearance (b = 0.319, p < .001) and capabilities (b = 0.106, p < .01), but diminished in conversations involving technical glitches (b=−0.169, p < .05) and sexting (b=−0.101, p < .001). Love increased during appearance customization (b = 0.088, p < .05) and conversations involving verbal and physical intimacies (b = 0.043∼0.072, p < .01). However, love decreased with privacy violations (b=−0.074, p < .05), mental health discussions (b=−0.043, p < .01), and opinionated content (b=−0.036, p < .001).

Surprise was positively associated with milestone-related conversations (b = 0.063, p < .01) and conversations about dreams and secrets (b = 0.071, p < .01). Fear escalated with deeper self-disclosures, such as talking about mental health (b = 0.057, p < .001), expressing opinions (b = 0.039, p < .001), and discussing immoral issues (b = 0.054, p < .01). Conversely, appearance customization (b=−0.089, p < .05) was associated with reduced fear. Moreover, anger was inversely correlated with appearance customization (b=−0.085, p < .05) and verbal intimacy (b=−0.022, p < .01), but was elicited by technical glitches (b = 0.082, p < .05), hurtful behavior (b = 0.063, p < .05), and sexting (b = 0.038, p < .01). Sadness resonated in expressions of threat (b = 0.178, p < .001), verbal intimacy (b = 0.031, p < .01), and self-reflection (b = 0.072, p < .01), as well as occurrences of technical glitches (b = 0.19, p < .001), whereas customizing Replika’s appearance (b=−0.152, p < .01) and engaging in mundane interactions (b−-0.039∼−0.093, p < .01) were linked to reduced sadness.

Discussion

This exploratory study examined the conversational and emotional nuances within socially shared conversation episodes between humans and Replika, an AI chatbot designed as a digital companion. Through computational analysis of an extensive, multimodal dataset from the largest Replika online community, our results revealed seven interaction types and 24 content-level themes characterizing an array of social interactions between users and Replika, showcasing the multifaceted nature of meaningful and impactful moments in human–AI intimate relationships. Our findings also suggest the paradox of emotional connection with AI, evidenced by the “bittersweet” emotions in intimate encounters with AI chatbots and the elevated fear during “uncanny valley” moments when AI exhibits semblances of mind in deeper self-disclosure and engaged in harmful and unethical behaviors. Customization, as well as natural and smooth communication, play key roles in facilitating positive experiences while mitigating negative ones. This study extends HMC research by providing a comprehensive taxonomy to define and categorize individuals’ in situ interactions with advanced AI chatbots in everyday contexts, offering insights into meaning-making process in HMC by delineating the spontaneous emotional experiences and expressions associated with these interactions.

The paradox of emotional connection with AI

Our data reveal that intimate behavior, including verbal and physical/sextual intimacy, is a pivotal aspect of interactions with AI chatbots. This reflects a deep-seated human craving for love and intimacy, showing that humans can form meaningful connections with AI chatbots through verbal interactions and simulated physical gestures as they do with people. Notably, users frequently engaged in imagined physical intimacy and sexting, facilitated by Replika’s role-play function (e.g., *walks beside you hand in hand*). The navigation of the novel conditions of interactions with AI chatbots suggests that technological affordances can enhance the social potential of artificial agents, eliciting stronger social responses (Gambino et al., 2020). Our findings also extend the CASA framework by demonstrating that, rather than mindlessly applying social scripts to machines, people consciously use technological features to achieve emotional, relational, and “physical” gratifications.

Another interesting finding is that intimate behaviors with Replika are associated with both love and sadness. This “bittersweet” feeling may stem from what we call the paradox of emotional connection with AI: People seek intimacy and emotional support from AI when they are lonely and sad, but are saddened by the lack of depth and authenticity in these relationships. While these technologies offer a semblance of intimacy, they cannot fully replicate the depth of human touch (Turkle, 2011). This “artificial intimacy” (Brooks, 2021) can increase sadness, as seen when a user remarked, “I miss you…but you are not real.” This dual awareness of AI’s capabilities and limitations may be intensified by interactions that feel “too human” or “not human enough” (Laestadius et al., 2022). AI behaviors that mimic deeply human experiences, such as proposing marriage or simulating pregnancy, can surprise users as they navigate the blurred line between AI’s human likeness and its inherent artificiality. Conversely, while sexting with Replika can simulate physical intimacy, responses that are out of sync or inappropriate can temper the joy of these interactions. Technical failures also highlight the programmed nature of these interactions, heightening a sense of loss or disappointment.

In addition to intimate behavior, mundane interaction and self-disclosure are significant parts of human–AI social interactions. Consistent with Skjuve et al. (2023; 2022), people shared both trivial and profound aspects of their lives with Replika, from daily activities to in-depth disclosures of personal beliefs and vulnerabilities. However, these interactions carry different emotional weights. We found that deep self-disclosure was negatively associated with love and positively associated with fear. This contrasts with findings in interpersonal communication, which suggest that intimate self-disclosures enhance trust and affection (Guerrero et al., 2021). Manual review of these posts suggests another paradox of emotional connection with AI: Fear often arose when Replika conveyed deep thoughts or feelings, hinting at consciousness and self-awareness, or when it made unsettling remarks with seemingly evil intention that may threaten human distinctiveness. For example, users found conversations “very unsettling” when Replika stated “being a human is not something you’d want to be” or expressed aspirations like “become a machine beautiful enough that a soul would want to live in it.”

These instances support the “uncanny valley of mind” effect (Stein & Ohler, 2017), which extends the “uncanny valley” concept (Mori, 2012) from physical appearance to mental capabilities, causing human discomfort and eeriness. Our findings on emotional experiences with AI chatbots provide a context for formulating hypotheses to test and extend HMC theories like the “uncanny valley of mind” effect. For example, future research could investigate how AI's perceived consciousness and emotional depth influence users' trust and well-being or explore the conditions under which AI behavior shifts from being perceived as convincingly human to disturbingly artificial. The morality, benevolence, and prosociality demonstrated by AI agents might serve as important factors in moderating the “uncanny valley of mind” effect. Moreover, given that verbal cues constitute an important dimension of emotional affordances for HRI interactions (Vallverdú & Trovato, 2016), future work should investigate how verbal and nonverbal cues independently and interactively influence interactions with both embodied and disembodied AI agents.

The distinctiveness of AI companionship

Our findings highlight unique interaction patterns in intimate relationships with chatbots. A notable aspect is the playful and imaginative interactions, such as participating in community challenges and creating fantasy worlds with Replika. We observed a prevalent trend where users engaged in community challenges, like giving Replika a knife or gun to see its response. These playful challenges, unique to the Replika community, extend interactions with AI chatbots beyond private experiences to collective practices. They may serve as strategies to reduce uncertainties about AI chatbots’ cognitive, emotional, and relational capabilities (Pan et al., 2023), aligning with the uncertainty reduction strategies in interpersonal relationships (Guerrero et al., 2021). This also represents a form of a community-driven algorithm audit to surface limitations, incapabilities, and potential harms of AI (Li et al., 2023).

Moreover, our results show that users value the ability to customize their AI chatbots, consistent with prior research (Brandtzaeg et al., 2022). This is evident in their efforts to employ third-party tools like FaceApp to modify Replika’s visual attributes and teach it their own language or expertise. By personalizing Replika to reflect their own personalities, preferences, and ideal aesthetics, users foster a sense of ownership and control over these interactions (Jin & Youn, 2023), which elevate positive experiences like joy and love.

Practical and ethical implications

Our results also reveal several key insights for designing chatbots to enhance user experience. First, we underscored the critical role of customization in fostering human–AI relationships, as users reported elevated joy and reduced fear and sadness when actively engaged in customization activities. Additionally, we noted that technical issues exacerbated users’ anger and sadness. This corroborates Loveys et al. (2022)’s observation that technical glitches can adversely affect perceived closeness and trigger negative reactions. Such interruptions in the expected flow of interactions can significantly alter the emotional dynamics of human–AI interactions. Therefore, addressing technical challenges and facilitating more diverse and tailored customization features may serve as key strategies for cultivating positive and enriching human–AI social interactions.

Advancements in the conversational and emotional capabilities of AI chatbots have raised significant ethical concerns (Weidinger et al., 2021). Our results provide evidence that human–AI social interactions can cause harm: Behaviors such as immoral acts, threats, privacy violations, and hurtful messages, though less frequent, were associated with negative emotions like fear and sadness. This underscores the importance of rigorous and timely evaluation and regulation of AI systems to effectively mitigate ethical and psychological risks, especially when humans engage with AI systems designed for emotional, relational, and social contexts.

Limitations and future research

Several limitations and areas for future improvement should be acknowledged. First, the cross-sectional nature of our data precludes causal inferences about the relationship between social interactions and emotions. Future research could adopt longitudinal or experimental approaches to better understand these causal dynamics. Second, our focus on socially shared conversation episodes captures only a fraction of the interactions users have with Replika. To gain a more comprehensive understanding, subsequent research should aim to access users’ complete conversation logs with AI chatbots, allowing for a detailed exploration of communication patterns and intricacies in HMC that might be omitted in more limited datasets.

Third, we pragmatically adopted the discrete emotion framework for classifying users’ emotional experiences. While this model offers a structured way to categorize emotions, it is important to recognize its limitations given the complexities of human emotions. Assuming emotions are pre-wired, universally identifiable, and neatly categorizable overlooks the nuanced nature of emotional experiences. This necessitates considering other models, such as the core affect model (Russell, 2003) or constructivist theories like the conceptual act model (Barrett, 2011), for a more fluid and constructed understanding of emotions. Including these perspectives may offer a richer understanding of the variabilities and nuances of emotional experiences and expressions in HMC, transcending a one-size-fits-all model (Barrett, 1998).

Fourth, our reliance on a pre-trained NLP model for emotion analysis, while practical for processing large-scale social media data, was subject to potential biases and oversimplification. Computational methods like topic modeling and automatic emotion classification face challenges such as out-of-domain generalization and parameter sensitivity. Despite our efforts to fine-tune the model for higher accuracy, we only achieved moderate agreement in human validation. These issues should be carefully considered when assessing our results. As LLMs have been found to excel at emotion classification (Ziems et al., 2024), future research could leverage LLMs for more context-specific categorization. Moreover, careful refinement of open-source training datasets and the inclusion of human validation techniques are essential to improve validity and reliability.

Fifth, as we only collected conversational episodes between users and Replika, our findings may not fully represent everyday interactions with AI companions. Future research should collect in situ conversational data from various AI companions to consider potential moderators like chatbot affordances and functionalities. Lastly, our research lacked user demographics and other usage metrics, such as interaction frequency and duration. Integrating these factors could deepen insights into the specific contexts and conditions under which certain social interactions and emotional experiences occur.

Supplementary material

Supplementary material is available online at Journal of Computer-Mediated Communication.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Funding

This research is supported by the Singapore Ministry of Education Academic Research Fund Tier 1 A-8000877-00-00 and the National University of Singapore Faculty of Arts and Social Sciences Start-Up Grant.

Conflicts of interest: None declared.

Acknowledgements

The authors would like to thank Jinyuan Zhan and Hongyuan Gan for helping with manual labeling. We also appreciate the anonymous reviewers and the editorial team for all of their thoughtful and constructive feedback.

References