Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Lu Y

PROVIDER: S-EPMC7819780 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lu Yingjie Y Luo Shuwen S Liu Xuan X

JMIR medical informatics 20210107 1

<h4>Background</h4>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.<h4>Objective</h ...[more]

PMID: 33279878

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Project description:BackgroundWhile online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge.ObjectiveThe objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs.MethodsWe initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features.ResultsThe best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others.ConclusionsWe developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Project description:BackgroundOnline health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts.ObjectiveThis study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC.MethodsWe collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn.ResultsUsers' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC.ConclusionsDetecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.

Project description:ObjectiveInformational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.MethodsA multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.ResultsThe results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.ConclusionsThis study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.

Project description:BackgroundStudies continually show benefits of active learning in college classrooms, yet it is difficult to get faculty to adopt these methods. Particularly challenging is the final step of the instructional change process, "refreezing," when after making initial changes in instructional methods, instructors decide whether to continue with new instructional methods or return to their previous methods. Though this stage is important, it is not well studied. Most available studies about ongoing support following professional development on teaching merely state that facilitators made an effort to offer support, or report how frequently participants engaged with online support mechanisms through counting postings on listservs or message boards. Such measures do not show evidence that participants actually received positive reinforcement or intellectual and emotional support, which are crucial to refreezing, nor do these frequency analyses help other professional developers learn how to create productive ongoing support mechanisms that yield high participant engagement.ResultsThis workshop for 35 college mathematics instructors used online and in-person communities to provide support to participants during the post-workshop period of "refreezing." Almost all workshop attendees participated in "e-mentoring" (94%), primarily through a productive, engaging group email listserv. By combining qualitative coding of message content with the techniques of social network analysis, we reveal how facilitators and participants on the group listserv provided intellectual and emotional support, as well as positive reinforcement through feedback loops. The analysis also shows how the facilitators made this a helpful group and maintained participant engagement through frequent encouragement, deliberate community building, and thoughtfully timed responses.ConclusionsThough many professional development workshops offer online support through email listservs, there is little evidence that these listservs successfully engage and support participants. Applying the analytic approach of social network analysis allowed us to model the conversation threads in one highly engaged and supportive listserv following a mathematics professional development workshop. This method revealed the processes of ongoing support in ways that traditional frequency-based analyses cannot. This method also revealed lessons for how other professional developers can create productive, helpful online support listservs. Since this is an innovative application of social network analysis, we describe the method in detail.

Project description:ObjectivesOnline support can be a crucial source of support for individuals experiencing suicidal behaviours, with forum moderators being pivotal in terms of the role they play in times of personal mental health emergencies. This study identified what is empirically known about the professional practices of health professionals who are online mental health forum moderators and provide support to individuals experiencing suicidal behaviours.DesignThe Levac, Colquhoun and O'Brien extension of the Arksey and O'Malley scoping review framework was used.Search strategyThe Psychology Collection (EBSCO), PsycINFO (EBSCO), Web of Science, Taylor and Francis Online, SAGE Journals and Science Direct databases were searched for articles that featured a result relating to an online forum; included participants who worked as online moderators or facilitators and focused on suicide or self-harm. Results were limited to peer-reviewed articles published in English from 1990 onwards. As a quality assurance measure, grey literature (nonacademic literature) was not included. Reference lists of included articles were hand-searched.ResultsThere were 397 articles initially identified after applying inclusion and exclusion criteria, with five articles included for synthesis. All articles received a moderate quality rating. Only one article featured a moderator who was a qualified health professional; the moderators in the remaining articles were volunteers who undertook preservice training. We found that there is little research that examines the professional working practices of online moderators who support individuals experiencing suicidal behaviours.ConclusionsThe dearth of research focusing on the professional practices of online forum moderators is cause for concern given that individuals experiencing suicidal behaviours are increasingly turning to online forums when in crisis. Future research should focus on online moderators' practice through interviewing moderators about their professional practices and by examining online moderator practice as it occurs in situ.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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