Community Structure of a Mental Health Internet Support Group: Modularity in User Thread Participation.
Community Structure of a Mental Health Internet Support Group: Modularity in User Thread Participation.
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DOI：
10.2196/mental.4961
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发表时间：
2016-05-30
影响因子：
5.2
通讯作者：
Griffiths KM
中科院分区：
文献类型：
--
作者：
Carron-Arthur B;Reynolds J;Bennett K;Bennett A;Cunningham JA;Griffiths KM
人们对心理健康互联网支持团体的社区结构知之甚少。需要更好地了解导致用户交互的因素，以解释这些服务的设计信息以及有关其实用性的未来研究。进行了一项研究，以确定与心理健康问题互联网支持小组的亚组社区结构相关的用户特征。对互联网支持小组 BlueBoard (blueboard.anu.edu.au) 进行了社交网络分析，以确定使用 Louvain 方法的社区的模块化程度。 BlueBoard 用户的人口统计特征包括年龄、性别、居住地点、用户类型（消费者、护理人员或其他）、注册日期和子论坛中的发帖频率（抑郁症、广泛性焦虑症、社交焦虑症、恐慌症、双相情感障碍、强迫症、边缘性人格障碍、饮食失调、护理人员、一般情况（例如“闲聊”）和建议框），作为最终亚组结构的潜在预测因素。模块化分析确定了 BlueBoard 社区中的五个主要子组。通过多项逻辑回归观察发现，注册日期是对模块化结果影响最大的因素。将该变量添加到包含所有其他因素的最终模型中，其分类精度提高了 46.3%，即从 37.9% 提高到 84.2%。对该变量的进一步调查显示，最活跃和最集中的用户注册时间明显早于每组的中位注册时间。这五个小组类似于不同时代的五代 BlueBoard，超越了对不同心理健康问题的讨论。这一发现可能是由于与许多其他用户进行交流的高度参与的中心用户的活动所致。未来的研究应寻求确定这一发现的普遍性，并调查高度活跃和核心的用户在这一现象的形成中可能发挥的作用。
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.