Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across intervention contexts, giving rise to distinct chatbot roles such as neutral mediators, message coaches, repair facilitators, and emotion regulators. Across these roles, participants positioned chatbots as moral advisors that evaluate communicative appropriateness and exercise varying degrees of moral authority. Rather than prescribing specific system behaviors, this work offers a conceptual and exploratory account of AI-mediated intervention in intergenerational communication, and articulates key design tensions that arise when chatbots are imagined as socially and morally involved actors in intimate family interactions.

AI-powered companion chatbots (AICCs) such as Replika are increasingly popular, offering empathetic interactions, yet their psychosocial impacts remain unclear. We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences. First, we conducted a large-scale quasi-experimental study of longitudinal Reddit data, applying stratified propensity score matching and Difference-in-Differences regression. Findings revealed mixed effects—greater grief expression and interpersonal focus, alongside increases in language about loneliness, depression, and suicidal ideation. Second, we complemented these results with 18 semi-structured interviews, which we thematically analyzed and contextualized using Knapp’s relationship development model. We identified trajectories of initiation, escalation, and bonding, wherein AICCs provided emotional validation and social rehearsal but also carried risks of over-reliance and withdrawal. Triangulating across methods, we offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages—maximizing psychosocial benefits while mitigating risks.

Mental-health stigma remains a pervasive social problem that hampers treatment-seeking and recovery. Existing resources for training neural models to finely classify such stigma are limited, relying primarily on social-media or synthetic data without theoretical underpinnings. To remedy this gap, we present an expert-annotated, theory-informed corpus of human-chatbot interviews, comprising 4,141 snippets from 684 participants with documented socio-cultural backgrounds. Our experiments benchmark state-of-the-art neural models and empirically unpack the challenges of stigma detection. This dataset can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. Our corpus is openly available at https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus.

Chatbots are increasingly integrated into people's lives and are widely used to help people. Recently, there has also been growing interest in the reverse direction-humans help chatbots-due to a wide range of benefits including better chatbot performance, human well-being, and collaborative outcomes. However, little research has explored the factors that motivate people to help chatbots. To address this gap, we draw on the Computers Are Social Actors (CASA) framework to examine how chatbot anthropomorphism-including human-like identity, emotional expression, and non-verbal expression-influences human empathy toward chatbots and their subsequent prosocial behaviors and intentions. We also explore people's own interpretations of their prosocial behaviors toward chatbots. We conducted an online experiment (N = 244) in which chatbots made mistakes in a collaborative image labeling task and explained the reasons to participants. We then measured participants' prosocial behaviors and intentions toward the chatbots. Our findings revealed that human identity and emotional expression of chatbots increased participants' prosocial behavior and intention toward chatbots, with empathy mediating these effects. Qualitative analysis identified two motivations for participants' prosocial behaviors: empathy for the chatbot and perceiving the chatbot as human-like. We discuss the implications of these results for understanding and promoting human prosocial behaviors toward chatbots.

Conversational Agents (CAs) have increasingly been integrated into everyday life, sparking significant discussions on social media. While previous research has examined public perceptions of AI in general, there is a notable lack in research focused on CAs, with fewer investigations into cultural variations in CA perceptions. To address this gap, this study used computational methods to analyze about one million social media discussions surrounding CAs and compared people’s discourses and perceptions of CAs in the US and China. We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions. In contrat, US participants saw CAs more functionally, with an ambivalent attitude. Warm perception was a key driver of positive emotions toward CAs in both countries. We discussed practical implications for designing contextually sensitive and user-centric CAs to resonate with various users’ preferences and needs.

There has been growing concern about digital well-being, especially given the emerging adverse impact of technology overuse. While prior studies have developed a variety of stand-alone techniques to combat technology overuse, little work has been done to build interventions directly into technologies to regulate usage. In this study, we designed three interventions that remind players to take a break in a casual mobile game. We explored players’ experiences with these interventions through a 4-day deployment study and follow-up interviews (N=16). Findings suggest that while some players had a positive experience with the game that had built-in interventions, others experienced unintended outcomes such as disrupted immersion or longer play sessions. We also found that interventions seemed to be more likely to succeed when players experienced a sense of accomplishment or negative emotions. We discuss the implications of the study and provide preliminary suggestions for designing built-in interventions.

Understanding how and why people share negative emotions and thoughts on social media has received much scholarly attention. Scholars have identified a variety of factors that affect disclosure behavior, but as platforms offer a wider range of affordances that enable more diverse user behaviors and nuanced audience segmentation, these influencing factors are increasingly intertwined. However, little is known about the interrelatedness of platform, affordance, and audience. Drawing on survey data of 470 American adults during the COVID-19 pandemic, this study examines the interplay and relative strength of the factors influencing distress disclosure on social media. We introduce the concept of social media disclosure ecology as an analytical lens to understand online disclosure. The results suggest that perceived affordances (i.e., anonymity, persistence, visibility control) and relational closeness to audience separately and interactively predict the depth of distress disclosure, which in turn affects satisfaction with disclosure. This study contributes to the literature on online-disclosure and privacy, while providing implications for the design of social media to better support people in distress.

A growing body of work has emphasized the need for customizability and flexibility in mobile health technologies to increase support user autonomy. However, customization may be burdensome for people with motivational and cognitive challenges, such as those with mental illnesses, and the optimal level and type of customizability are unclear. Based on 32 interviews with people who experience symptoms of depression and anxiety, we examine how individuals use and customize mental health apps to manage their symptoms. Our findings suggest that participants’ engagement with the apps is affected by their level of energy and motivation, depending on the severity of symptoms. Customization is deemed desirable when the required user effort does not exceed users’ mental and motivational capacity and when ample resources are available. We discuss how customizable systems can increase autonomy without overburdening users in the context of mental health.

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

The last decade has seen increased reports of mental health problems among college students, with college counseling centers struggling to keep up with the demand for services. Digital mental health tools offer a potential solution to expand the reach of mental health services for college students. In this paper, we present findings from a series of design activities conducted with college students and counseling center staff aimed at identifying needs and preferences for digital mental health tools. Results emphasize the social ecosystems and social support networks in a college student's life. Our findings highlight the predominant role of known peers, and the ancillary roles of unknown peers and non-peers (e.g., faculty, family) in influencing the types of digital mental health tools students desire, and the ways in which they want to learn about mental health tools. We identify considerations for designing digital mental health tools for college students that take into account the identified social factors and roles.