Online health community for change: Analysis of self-disclosure and social networks of users with depression

Abstract

BackgroundA key research question with theoretical and practical implications is to investigate the various conditions by which social network sites (SNS) may either enhance or interfere with mental well-being, given the omnipresence of SNS and their dual effects on well-being.Method/processWe study SNS’ effects on well-being by accounting for users’ personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.Result/conclusionWe compare users’ within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users’ self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users’ topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users’ received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.InnovationWe combine social network analysis with content analysis, highlighting the need to understand SNS’ effects on well-being by accounting for users’ self-disclosure (content) and communication partners (social networks).Implication/contributionAuthentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS’ effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.

Words for the hearts: a corpus study of metaphors in online depression communities

“…This is because Chinese culture traditionally emphasizes the importance of maintaining one’s public image and avoiding anything that would be seen as a sign of weakness that would damage the collective reputation of the family or community. Studies have supported the concept of sociocentric personhood in relation to mental disorders, as Chinese culture constructs and experiences the disorders in somatic or interperson settings ( Xu and Zhang, 2016 ; Yoon et al, 2019 ; Zou et al, 2020 ; Shi and Khoo, 2023 ). Many people fear appearing burdensome to their families or losing their face if they admit to having depression.…”

“…It can reinforce shame and isolation when individuals are conceptualized as defective, broken, or useless. Meanwhile, this finding suggests that metaphors with negative connotations should also be considered as critical self-disclosure attributes, despite the fact that most ready-made analytical tools (such as LIWC) rely on negative adjectives to detect online depressed users ( Shi and Khoo, 2023 ).…”

“…Researchers have found that using first-person singular pronouns, negative emotion words, and death words helps reveal depression symptoms ( Liu et al, 2018 ; Pan et al, 2020 ). In particular, first-person singular forms can help detect depression since self-references are more frequent among depressed people: a person experiencing physical or emotional pain tends to focus more on themselves and thus use more first-person singular forms ( Shi and Khoo, 2023 ). This paper finds a significant presence of metaphors in OHC that specifically conceptualize the individual with depression.…”

“…Alternatively, it may be a result of low treatment rates, slow healing rates, and high relapse rates ( Kronmüller et al, 2011 ; Lu et al, 2021 ; Yao et al, 2022 ). Accordingly, negative metaphors should also be considered critical self-disclosure attributes of this population, despite the fact that most ready-made analytical tools (such as LIWC) detect online depressed users using negative adjectives ( Shi and Khoo, 2023 ).…”

“…Studies have supported the concept of sociocentric personhood in relation to mental disorders, as Chinese culture constructs and experiences the disorders in somatic or interperson settings (Xu and Zhang, 2016;Yoon et al, 2019;Zou et al, 2020;Shi and Khoo, 2023). Many people fear appearing burdensome to their families or losing their face if they admit to having depression.…”