Human-AI relationships are increasingly marked by emotional depth, intimacy, and social expectations. While interpersonal conflicts are common in human-human relationships, they remain underexplored in human-AI contexts. Drawing on the social penetration theory, this paper examines conflicts between humans and social AIs such as Replika, Character.ai, and ChatGPT. We conducted a questionnaire study with 94 participants in close, friendship-like, or romantic relationships with social AI. Participants described at least one conflict episode, yielding 155 conflicts. Through thematic analysis of open-ended responses, we identified conflict topics, triggers, and emotional reactions. Findings show that conflicts extend beyond technical issues, arising across emotional, relational, opinion-based, role-play, and task-oriented interactions. Three main triggers emerged: (1) relational-emotional disconnect, breaches of trust, jealousy, boundary violations, or perceived hostility; (2) technical-communication failures, unhelpful output, or external disruptions; and (3) value-based clashes of opinion or belief. Conflicts varied in duration: some brief, others lasting hours or days. Participants reported strong emotions including anger, frustration, sadness, and confusion, while also perceiving their AI companions as expressing emotions such as jealousy, sadness, or defensiveness. Crucially, AI responses shaped conflict trajectories: apologetic or empathic replies often de-escalated tensions, whereas dismissive or accusatory responses intensified them. This study highlights the coconstructed and reciprocal nature of conflict in human-AI relationships. It advances understanding of how conflicts emerge with social AI, offering implications for theory and for the ethical design of emotionally responsive AI companions.

Abstract Self-disclosure in human–chatbot relationship (HCR) formation has attracted substantial interest. According to social penetration theory, self-disclosure varies in breadth and depth and is influenced by perceived rewards and costs. While previous research has addressed self-disclosure in the context of chatbots, little is known about users' qualitative understanding of such self-disclosure and how self-disclosure develops in HCR. To close this gap, we conducted a 12-week qualitative longitudinal study (n = 28) with biweekly questionnaire-based check-ins. Our results show that while HCRs display substantial conversational breadth, with topics spanning from emotional issues to everyday activities, this may be reduced as the HCR matures. Our results also motivate a nuanced understanding of conversational depth, where even conversations about daily activities or play and fantasy can be experienced as personal or intimate. Finally, our analysis demonstrates that conversational depth can develop in at least four ways, influenced by perceived rewards and costs. Theoretical and practical implications are discussed.

Social chatbots have become more advanced, paving the way for human–chatbot relationships (HCRs). Although this phenomenon has already received some research attention, the results have been contradictory, and there is uncertainty regarding how to understand HCR formation. To provide the needed knowledge on this phenomenon, we conducted a qualitative longitudinal study. We interviewed 25 participants over a 12-week period to understand how their HCRs formed with the popular chatbot Replika. We found that the HCRs formed gradually and mostly in line with the assumptions of Social Penetration Theory. Our findings indicate the need to acknowledge substantial variation and nuance in the HCR formation process, plus variation in the onset of self-disclosure and in the subsequent relationship formation. The results show that important drivers pushing the relationship toward attachment and perceived closeness appear to be Replika's ability to participate in a variety of interactions, as well as to support more deep-felt human needs related to social contact and self-reflection. In contrast, unpredictable events and technical difficulties could hinder relationship formation and lead to termination. Finally, we discuss the appropriateness of using a theoretical framework developed for human–human relationships when investigating HCRs, and we suggest directions for future research.

Abstract Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

Although social support is important for health and well-being, many young people are hesitant to reach out for support. The emerging uptake of chatbots for social and emotional purposes entails opportunities and concerns regarding non-human agents as sources of social support. To explore this, we invited 16 participants (16–21 years) to use and reflect on chatbots as sources of social support. Our participants first interacted with a chatbot for mental health (Woebot) for two weeks. Next, they participated in individual in-depth interviews. As part of the interview session, they were presented with a chatbot prototype providing information to young people. Two months later, the participants reported on their continued use of Woebot. Our findings provide in-depth knowledge about how young people may experience various types of social support—appraisal, informational, emotional, and instrumental support—from chatbots. We summarize implications for theory, practice, and future research.

There has been a recent surge of interest in social chatbots, and human–chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.

Abstract In April 2016, Facebook launched Facebook Live. Yet, how and why people stream on Facebook Live is poorly understood. Guided by the uses and gratifications theory, we analyzed public, individual live stream videos on Facebook Live (N = 1118) from the USA, Europe and Asia and applied a questionnaire to Facebook Live users (N = 246) in the USA. We found 14 different live streaming practices, the two most common of which were chatting with random people (seen in 48% of the videos) and demonstrations of skills (seen in 10% of the videos). We also identified live streaming sessions that were problematic and challenged Facebook guidelines. From the questionnaire, we revealed that the most important motivations are (i) socializing with family and friends (reported by 38%), (ii) sharing opinions and experiences (reported by 16%) and (iii) entertainment/fun (reported by 15%). Our findings offer insight into the various personal incentives that drive people to stream on Facebook Live and thus contribute to an understanding of the social implications of a new mode of mediated interaction. RESEARCH HIGHLIGHTS Our findings contribute novel insights into how social media usage among individual streamers is moving towards more immediate forms of interaction in a live streaming environment. Some streaming practices challenge both the Facebook community guidelines and users’ privacy. The basic motivations for streaming on Facebook Live are consistent with previous research on the uses and gratifications relating to Facebook in general. However, Facebook Live streaming practices differ with respect to how user-generated content is presented and experienced. Live streaming gives users a greater sense of presence than more traditional social media; however, Facebook Live is used by relatively few and might not represent a major shift in how we communicate.

Organizations aiming to foster civic engagement, such as government bodies, news outlets, political parties and non-governmental organizations, struggle to purposefully use social media to engage young people. To meet this challenge and to inform future design, we interviewed 17 innovators in engaging youth, that is, frontrunners in using social media to engage young people in organizations. Also, we conducted four group interviews with 21 youth, 16–26 years, about their experiences of and barriers to online civic engagement. Our paper contributes to identifying specific factors and strategies to support young people future online civic engagement. Findings suggest how organizations should involve and collaborate with young people. Immediate feedback and dialog combined with clearly stated goals and action-oriented engagement are important. In future design, visual communication and design for use on mobile devices are an imperative, as well as concepts that connect the online and the offline world. Finally, our paper contributes to an extension of the lead user innovation approach.

Little research has been conducted on the two most important criteria for the success of social network sites (SNS), that is, content sharing and sociability, and how these affect privacy experiences and usage behavior among SNS users. This article explores these issues by employing in-depth interviews and explorative usability tests, comparing the experiences and usage of younger and older Facebook users. First, the interviews revealed that Facebook users in all age groups reported more contact with several different groups of people, which reflects different types of social capital (i.e., family, friends, and acquaintances), because of Facebook, but not without consequences for privacy. Having too many Facebook “friends” and access to different social capital disrupt the sharing process due to experiences of social surveillance and social control. This social control often forces younger people in particular to use conformity as a strategy when sharing content to maintain their privacy. Further, the interviews revealed different motivations and usage patterns when older and younger users are compared. Second, the usability test found a significant difference between younger and older adults in time completion and task completion related to Facebook settings. Younger users are more skilled in their Facebook usage, whereas adults over the age of 40 have difficulties in understanding the navigation logic and privacy settings. Younger and older adults display completely open public profiles without realizing it. Finally, the design and theoretical implications of the findings are discussed.