National Institute for Mental Health Research, Research School of Population Health, The Australian National University, Acton, Australia.
JMIR Ment Health. 2016 May 30;3(2):e20. doi: 10.2196/mental.4961.
Little is known about the community structure of mental health Internet support groups, quantitatively. A greater understanding of the factors, which lead to user interaction, is needed to explain the design information of these services and future research concerning their utility.
A study was conducted to determine the characteristics of users associated with the subgroup community structure of an Internet support group for mental health issues.
A social network analysis of the Internet support group BlueBoard (blueboard.anu.edu.au) was performed to determine the modularity of the community using the Louvain method. Demographic characteristics age, gender, residential location, type of user (consumer, carer, or other), registration date, and posting frequency in subforums (depression, generalized anxiety, social anxiety, panic disorder, bipolar disorder, obsessive compulsive disorder, borderline personality disorder, eating disorders, carers, general (eg, "chit chat"), and suggestions box) of the BlueBoard users were assessed as potential predictors of the resulting subgroup structure.
The analysis of modularity identified five main subgroups in the BlueBoard community. Registration date was found to be the largest contributor to the modularity outcome as observed by multinomial logistic regression. The addition of this variable to the final model containing all other factors improved its classification accuracy by 46.3%, that is, from 37.9% to 84.2%. Further investigation of this variable revealed that the most active and central users registered significantly earlier than the median registration time in each group.
The five subgroups resembled five generations of BlueBoard in distinct eras that transcended discussion about different mental health issues. This finding may be due to the activity of highly engaged and central users who communicate with many other users. Future research should seek to determine the generalizability of this finding and investigate the role that highly active and central users may play in the formation of this phenomenon.
对于心理健康互联网支持群体的社区结构，人们知之甚少，尤其是从定量的角度。为了解释这些服务的设计信息，并为未来关于其效用的研究提供依据，我们需要更多地了解导致用户互动的因素。
本研究旨在确定与心理健康问题互联网支持组亚群社区结构相关的用户特征。
采用社会网络分析法对 BlueBoard（blueboard.anu.edu.au）互联网支持组进行分析，使用 Louvain 方法确定社区的模块性。评估 BlueBoard 用户的人口统计学特征（年龄、性别、居住地点、用户类型（消费者、照顾者或其他）、注册日期和在分论坛（抑郁、广泛性焦虑、社交焦虑、惊恐障碍、双相情感障碍、强迫症、边缘型人格障碍、饮食障碍、照顾者、一般（如“闲聊”）和建议箱）中的发帖频率）是否可能成为亚群结构的潜在预测因素。
模块化分析确定了 BlueBoard 社区的五个主要亚群。通过多项逻辑回归分析发现，注册日期是模块性结果的最大贡献者。将该变量添加到包含所有其他因素的最终模型中，可将其分类准确性提高 46.3%，即从 37.9%提高到 84.2%。进一步研究该变量发现，最活跃和中心的用户比每个群组的中位数注册时间更早注册。
这五个亚群类似于 BlueBoard 的五个不同时代的群体，跨越了不同心理健康问题的讨论。这一发现可能是由于高度活跃和中心的用户的活动所致，他们与许多其他用户进行了交流。未来的研究应努力确定这一发现的普遍性，并探讨高度活跃和中心的用户在形成这一现象中可能发挥的作用。
