Dataset Information

Social interactions in online eating disorder communities: A network perspective.

ABSTRACT: Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online. Based on a large set of tweets posted by individuals who self-identify with eating disorders online, we establish the existence of two communities: a large community reinforcing disordered eating behaviours and a second, smaller community supporting efforts to recover from the disease. We find that individuals tend to mainly interact with others within the same community, with limited interactions across communities and inter-community interactions characterized by more negative emotions than intra-community interactions. Moreover, by studying the associations between individuals' behavioural characteristics and interpersonal connections in the communication network, we present the first large-scale investigation of social norms in online health communities, particularly on how a community approves of individuals' behaviours. Our findings shed new light on how people form online health communities and can have broad clinical implications on disease prevention and online intervention.

SUBMITTER: Wang T

PROVIDER: S-EPMC6066201 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Social interactions in online eating disorder communities: A network perspective.

Wang Tao T Brede Markus M Ianni Antonella A Mentzakis Emmanouil E

PloS one 20180730 7

Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online. Bas ...[more]

PMID: 30059512

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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:BackgroundIn 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.ObjectiveOur 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.MethodsWe 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.ResultsWe 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.ConclusionsThis 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.

Dataset Information

Social interactions in online eating disorder communities: A network perspective.

Publications

Social interactions in online eating disorder communities: A network perspective.

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