文献类型：期刊文献

英文题名：Development of Social Support Networks by Patients With Depression Through Online Health Communities: Social Network Analysis

作者：Lu, Yingjie[1];Luo, Shuwen[1];Liu, Xuan[2]

机构：[1]Beijing Univ Chem Technol, Sch Econ & Management, Beijing, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Meilong Rd 130, Shanghai 200237, Peoples R China

年份：2021

卷号：9

期号：1

外文期刊名：JMIR MEDICAL INFORMATICS

收录：;WOS:【SSCI(收录号:WOS:000615044100020)，SCI-EXPANDED(收录号:WOS:000615044100020)】；

基金：This work was supported in part by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences under Grant 18YJC630117, in part by the Social Science Foundation of Beijing under Grant 19GLC064, in part by the National Natural Science Foundation of China under Grant 71971082, in part by the Key Soft Science Projects in Shanghai under Grant 19692106700, and in part by the Funds for First-class Discipline Construction under Grant XK1802-5.

语种：英文

外文关键词：online depression community; social support network; exponential random graph model; informational support; emotional support; mental health; depression; social network

摘要：Background: In recent years, people with mental health problems are increasingly using online social networks to receive social support. For example, in online depression communities, patients can share their experiences, exchange valuable information, and receive emotional support to help them cope with their disease. Therefore, it is critical to understand how patients with depression develop online social support networks to exchange informational and emotional support. Objective: Our aim in this study was to investigate which user attributes have significant effects on the formation of informational and emotional support networks in online depression communities and to further examine whether there is an association between the two social networks. Methods: We used social network theory and constructed exponential random graph models to help understand the informational and emotional support networks in online depression communities. A total of 74,986 original posts were retrieved from 1077 members in an online depression community in China from April 2003 to September 2017 and the available data were extracted. An informational support network of 1077 participant nodes and 6557 arcs and an emotional support network of 1077 participant nodes and 6430 arcs were constructed to examine the endogenous (purely structural) effects and exogenous (actor-relation) effects on each support network separately, as well as the cross-network effects between the two networks. Results: We found significant effects of two important structural features, reciprocity and transitivity, on the formation of both the informational support network (r=3.6247, P<.001, and r=1.6232, P<.001, respectively) and the emotional support network (r=4.4111, P<.001, and r=0.0177, P<.001, respectively). The results also showed significant effects of some individual factors on the formation of the two networks. No significant effects of homophily were found for gender (r=0.0783, P=.20, and r=0.1122, P=.25, respectively) in the informational or emotional support networks. There was no tendency for users who had great influence (r=0.3253, P=.05) or wrote more posts (r=0.3896, P=.07) or newcomers (r=-0.0452, P=.66) to form informational support ties more easily. However, users who spent more time online (r=0.6680, P<.001) or provided more replies to other posts (r=0.5026, P<.001) were more likely to form informational support ties. Users who had a big influence (r=0.8325, P<.001), spent more time online (r=0.5839, P<.001), wrote more posts (r=2.4025, P<.001), or provided more replies to other posts (r=0.2259, P<.001) were more likely to form emotional support ties, and newcomers (r=-0.4224, P<.001) were less likely than old-timers to receive emotional support. In addition, we found that there was a significant entrainment effect (r=0.7834, P<.001) and a nonsignificant exchange effect (r=-0.2757, P=.32) between the two networks. Conclusions: This study makes several important theoretical contributions to the research on online depression communities and has important practical implications for the managers of online depression communities and the users involved in these communities.