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Exploring How People Affected by Methamphetamine Exchange Social Support Through Online Interactions on Facebook: Content Analysis.

ABSTRACT: BACKGROUND:Methamphetamine is an illicit and addictive psychostimulant that remains to be a significant cause of economic burden in Australia. Social media is increasingly being used by nongovernment organizations and health services to encourage the growth of social support networks among people with health-related issues. Several studies have investigated the utility of social media in providing social support to groups of people with health-related issues. However, limited research exists that explores how people who have been directly or indirectly affected by methamphetamine use social media for social support. OBJECTIVE:This study aimed to determine the types of social support being sought and provided by people affected by methamphetamine when interacting with others on a Facebook page. METHODS:A total of 14,777 posts were collected from a Facebook page and transferred into an Excel document for content analysis. The posts were manually coded into categories of social support using an adapted version of Cutrona and Suhr's Social Support Behavior Code. Posts could be coded into more than one category. Saturation was reached at 2000 posts, which were used to draw inferences. RESULTS:Emotional support was the most offered support type, with 42.05% (841/2000) of posts providing this form of support. This is followed by esteem support, which was provided in 40.40% (808/2000) of posts. Overall, 24.20% (484/2000) of posts offered informational support. Network support and tangible support were the least offered support types, with 2.25% (45/2000) and 1.70% (34/2000) of posts offering these types of support, respectively. CONCLUSIONS:This study suggests that online social support groups can be effective in challenging stigma by encouraging people affected by methamphetamine to connect with each other and talk about their struggles. This in turn represents an important step toward successful rehabilitation.

SUBMITTER: Ellway D

PROVIDER: S-EPMC6774572 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Exploring How People Affected by Methamphetamine Exchange Social Support Through Online Interactions on Facebook: Content Analysis.

Ellway Daniel D Reilly Rachel R Le Couteur Amanda A Ward James J

JMIR mental health 20191001 10

<h4>Background</h4>Methamphetamine is an illicit and addictive psychostimulant that remains to be a significant cause of economic burden in Australia. Social media is increasingly being used by nongovernment organizations and health services to encourage the growth of social support networks among people with health-related issues. Several studies have investigated the utility of social media in providing social support to groups of people with health-related issues. However, limited research ex ...[more]

PMID: 31573926

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Project description:BackgroundA key factor in successfully reducing and quitting smoking, as well as preventing smoking relapse is access to and engagement with social support. Recent technological advances have made it possible for smokers to access social support via online community forums. While community forums associated with smoking cessation interventions are now common practice, there is a gap in understanding how and when the different types of social support identified by Cutrona and Suhr (1992) (emotional, esteem, informational, tangible, and network) are exchanged on such forums. Community forums that entail "superusers" (a key marker of a successful forum), like QuitNow, are ripe for exploring and leveraging promising social support exchanges on these platforms.ObjectiveThe purpose of this study was to characterize the posts made on the QuitNow community forum at different stages in the quit journey, and determine when and how the social support constructs are present within the posts.MethodsA total of 506 posts (including original and response posts) were collected. Using conventional content analysis, the original posts were coded inductively to generate categories and subcategories, and the responses were coded deductively according to the 5 types of social support. Data were analyzed using Microsoft Excel software.ResultsOverall, individuals were most heavily engaged on the forum during the first month of quitting, which then tapered off in the subsequent months. In relation to the original posts, the majority of them fit into the categories of sharing quit successes, quit struggles, updates, quit strategies, and desires to quit. Asking for advice and describing smoke-free benefits were the least represented categories. In relation to the responses, encouragement (emotional), compliment (esteem), and suggestion/advice (informational) consistently remained the most prominent types of support throughout all quit stages. Companionship (network) maintained a steady downward trajectory over time.ConclusionsThe findings of this study highlight the complexity of how and when different types of social support are exchanged on the QuitNow community forum. These findings provide directions for how social support can be more strategically employed and leveraged in these online contexts to support smoking cessation.

Project description:BACKGROUND:While 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. OBJECTIVE:The 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. METHODS:We 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. RESULTS:The 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. CONCLUSIONS:We 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.

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Exploring How People Affected by Methamphetamine Exchange Social Support Through Online Interactions on Facebook: Content Analysis.

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Exploring How People Affected by Methamphetamine Exchange Social Support Through Online Interactions on Facebook: Content Analysis.

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