Chatbots are increasingly being used in mental healthcare - e.g., for assessing mental-health conditions and providing digital counseling - and have been found to have considerable potential for facilitating people's behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants' statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants' comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots' conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants' stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot's self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users' perceptions of the chatbots themselves, and how chatbots' interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.

Chatbots have been designed to provide interventions in mental healthcare. However, how chatbot-based social contact can mitigate social stigma in mental illness remains under-explored. We designed two chatbots that deliver either first-person or third-person narratives about mental illness and evaluated them using a mixed methods study. Compared to a web survey group, participants in both chatbot groups decreased their beliefs that individuals are personally responsible for their mental illnesses, and increased their intentions to help. Additionally, participants in the first-person chatbot group showed a reduced level of fear, and a lower desire for social distance from people with mental illness. Many in the first-person chatbot group also reported a feeling of relationship with the chatbot, and chose to phrase their responses empathetically. Results demonstrated that chatbot-based social contact has promising potential for mitigating mental illness stigma. Implications for designing chatbot-based social contact are discussed.

Sexual-minority women (SMWs) in China are often subject to strong stigmatization and tend to have limited opportunities to connect with other SMWs in offline contexts. Although dating apps help them connect and seek social support, little is known about SMWs’ practices of self-disclosure and connection-building through those apps. To address this gap, we interviewed 43 SMW dating-app users in China. We found that these SMWs developed distinctive self-disclosure strategies, such as posting non-facial photos and implicitly disclosing their whereabouts by blending location information into photos that only those in the know could understand, to avoid interference from aggressive acquaintances and other risks of unintentional disclosure of their SMW identities. Moreover, they used dating apps not only to recognize other SMWs offline and build relationships with them, but to exchange emotional support in the process of SMW identity development. Our findings have design implications for supporting SMWs and improving their online dating experiences.

In China, lesbian and bisexual women face intense stigma and difficulties developing relationships with each other. Although prior research has shown that online communities help LGBT people connect and exchange social support, few studies have explored the challenges Chinese lesbian and bisexual women face when initiating, growing, and sustaining such communities, in an atmosphere of platform censorship of LGBT-related content and intense discrimination from non-LGBT people. To address this gap, we interviewed 40 Weibo users in China, four bloggers and 36 followers of their blogs, who self-identified as lesbian or bisexual women. We found that a key technique these bloggers used to initiate their online communities was helping followers publish posts seeking support, sharing personal experiences, and seeking offline relationships. Then, their followers built relationships with bloggers by journaling their daily experiences as lesbian or bisexual women via private-messaging channels. As the communities' members grew more attached to them, bloggers and their followers began to work together to protect themselves from external threats, including Weibo's censorship and non-LGBT+ infiltrators' harassment. However, such attachment to the communities sometimes might lead to conflicts within them, which in turn prompted many members to leave, raising questions about the communities' long-term prospects. Our findings foreground important design considerations for those seeking to help lesbian and bisexual women in China and other discriminatory environments to develop safe online communities.