ArticleJournal of medical Internet research2020
Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Social Support Mechanisms in an Online Type 1 Diabetes Community: Social Network Analysis of Stakeholder Diversity and Disease Duration.Journal of medical Internet research · 2026Observational
- The association of social networks with the job performance of primary health care professionals: the mediating effect of knowledge sharing.Frontiers in medicine · 2024Article
- How Do Team-Level and Individual-Level Linguistic Styles Affect Patients' Emotional Well-Being-Evidence from Online Doctor Teams.International journal of environmental research and public health · 2023Article
- Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.JMIR formative research · 2023Article
- U-shaped association between online information exchange and app usage frequency: a large-scale survey of China 's online young and middle-aged people with pre diabetes and diabetes.Frontiers in endocrinology · 2023Article
- Exploring the User Interaction Network in an Anxiety Disorder Online Community: An Exponential Random Graph Model with Topical and Emotional Effects.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough an increasing number of studies have attempted to understand how people interact with others in web-based health communities, studies focusing on understanding individuals' patterns of information exchange and social support in web-based health communities are still limited. In this paper, we discuss how patients' social interactions develop into social networks based on a network exchange framework and empirically validate the framework in web-based health care community contexts.
objectiveThis study aims to explore various patterns of information exchange and social support in web-based health care communities and identify factors that affect such patterns.
methodsUsing social network analysis and text mining techniques, we empirically validated a network exchange framework on a 10-year data set collected from a popular web-based health community. A reply network was extracted from the data set, and exponential random graph models were used to discover patterns of information exchange and social support from the network.
resultsResults showed that reciprocated information exchange was common in web-based health communities. The homophily effect existed in general conversations but was weakened when exchanging knowledge. New members in web-based health communities tended to receive more support. Furthermore, polarized sentiment increases the chances of receiving replies, and optimistic users play an important role in providing social support to the entire community.
conclusionsThis study complements the literature on network exchange theories and contributes to a better understanding of social exchange patterns in the web-based health care context. Practically, this study can help web-based patients obtain information and social support more effectively.
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Registered trials
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