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arXiv · 2409.14859

MentalImager: Exploring Generative Images for Assisting Support-Seekers' Self-Disclosure in Online Mental Health Communities

Han Zhang·Jiaqi Zhang·Yuxiang Zhou·Ryan Louie·Taewook Kim·Qingyu Guo·Shuailin Li·Zhenhui Peng
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Abstract

Support-seekers' self-disclosure of their suffering experiences, thoughts, and feelings in the post can help them get needed peer support in online mental health communities (OMHCs). However, such mental health self-disclosure could be challenging. Images can facilitate the manifestation of relevant experiences and feelings in the text; yet, relevant images are not always available. In this paper, we present a technical prototype named MentalImager and validate in a human evaluation study that it can generate topical- and emotional-relevant images based on the seekers' drafted posts or specified keywords. Two user studies demonstrate that MentalImager not only improves seekers' satisfaction with their self-disclosure in their posts but also invokes support-providers' empathy for the seekers and willingness to offer help. Such improvements are credited to the generated images, which help seekers express their emotions and inspire them to add more details about their experiences and feelings. We report concerns on MentalImager and discuss insights for supporting self-disclosure in OMHCs.

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Han Zhang, Jiaqi Zhang, Yuxiang Zhou, Ryan Louie, Taewook Kim, Qingyu Guo, Shuailin Li, Zhenhui Peng. 2024-09-23. MentalImager: Exploring Generative Images for Assisting Support-Seekers' Self-Disclosure in Online Mental Health Communities. https://arxiv.org/abs/2409.14859

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