An Algorithmically Woven Community: Disclosing Mental Illness and Connecting with Similar Others on an Algorithm-Mediated Platform in China

Abstract

Nowadays, algorithms are extensively used on social platforms for content recommendations and user connections. In this study, through semi-structured interviews with 22 China-based users of RED (i.e., an algorithm-mediated platform similar to TikTok), we investigate how users who have experienced mental illnesses (depression, anxiety, and bipolar disorder) understand RED algorithms and how these algorithms shape their self-disclosure, imagined audience, and community building. Specifically, both the norms of self-disclosure and content recipients are governed by algorithms, while users with limited agency form various folk theories to navigate the process. Moreover, based on accurate content recommendations, the algorithms of RED enable users who have experienced mental illnesses to connect with similar others across the platform. By proposing the concept of an “algorithmically woven community,” we conceptualize and visualize a novel mechanism of how an algorithm works akin to a responsive authority to weave a loosely knit, decentralized, and boundless network.