Dataset Information

Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.

ABSTRACT: BACKGROUND:Although 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. OBJECTIVE:This 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. METHODS:Using 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. RESULTS:Results 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. CONCLUSIONS:This 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.

SUBMITTER: Liu X

PROVIDER: S-EPMC7556372 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.

Liu Xuan X Jiang Shan S Sun Min M Chi Xiaotong X

Journal of medical Internet research 20200929 9

<h4>Background</h4>Although 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 commu ...[more]

PMID: 32990628

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Project description:BackgroundThe COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI).ObjectiveBuilding off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique.MethodsIn September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county's total population, percent rurality, and distance between each county pair.ResultsWe found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county's total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26).ConclusionsThese results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.

Dataset Information

Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.

Publications

Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models.

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