Social Support and Exchange Patterns in an Online Smoking Cessation Intervention Program

Zhang, Mi; Yang, Christopher C.; Gong, Xiaolin

doi:10.1109/ichi.2013.37

Cited by 21 publications

(19 citation statements)

References 22 publications

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“…For instance, the qualitative study by Zhang et al [14] found that users exchange informational support more frequently than other types of social support in an OHC for smoking cession. Nambisan [15] indicated informational support and social support existing in OHCs, but the information seeking effectiveness affects patient's perceived empathy.…”

Section: Introductionmentioning

confidence: 99%

Social Support and User Engagement in Online Health Communities

Wang

Zhao

Street

2014

Lecture Notes in Computer Science

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Abstract. Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with those who face similar problems and are involved in different types of social supports, such as informational support, emotional support and companionship. Using a case study of an OHC among breast cancer survivors, we first use machine learning techniques to reveal the types of social support embedded in each post from an OHC. Then we generate each user's contribution profile by aggregating the user's involvement in various types of social support and reveal that users play different roles in the OHC. By comparing online activities for users with different roles and conducting survival analysis on users' time span of online activities, we illustrate that users' levels of engagement in an OHC are related to various types of social support in different ways.

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Section: Introductionmentioning

confidence: 99%

Social Support and User Engagement in Online Health Communities

Wang

Zhao

Street

2014

Lecture Notes in Computer Science

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“…Many studies use qualitative analysis to analyze the content of user discussion and develop different classification schemes [7][8][9]. Our previous research focused on user interactions of social support exchange, and extracted five themes from messages on QuitStop forum, including offering social support, requesting social support, receiving social support, other activities and irrelevant content [7].…”

Section: A Online Communities Of Health Interventionmentioning

confidence: 99%

“…Our previous research [7] extracted five different themes from messages (including posts and comments) of QuitStop, including offering social support, requesting social support, receiving social support, other activities of smoking cessation, and irrelevant content. These themes reflect user intentions of publishing messages.…”

Section: Introductionmentioning

confidence: 99%

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Classifying User Intention and Social Support Types in Online Healthcare Discussions

Zhang

Yang

2014

2014 IEEE International Conference on Healthcare Informatics

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With the development of online healthcare social media, a large volume of user generated content are available in different health discussion forums or groups. Healthcare social media sites could empower patients to play a substantial role in their treatment by acquiring knowledge and support through actively involving in online discussions and interactions. However, users may have difficulty to find relevant topics or peers in these online health forums with a large amount of unstructured information. Most recommendation systems rely on content-based approach to recommend peers or discussions to their participants.However, in healthcare social media sites, content-based approach is not sufficient because health consumers may have different intentions of participation or may be interest in different types of support even if the content matches their interest. Based on previous studies, we utilize Naïve Bayes methods and propose two tasks for classifying posts and comments on QuitStop forum, an online community for smoking cessation intervention, respectively: (1) classification of intentions and (2) classification of social support types. Different text feature sets and user health feature sets are selected to develop classifiers. Taking different evaluation indicators as optimizing goals, we develop genetic algorithms to combine classifiers with different feature sets and optimize the classification results. It is found that for post classification, integrating text and health features could achieve the highest precision, recall and F1 measure. For comment classification, combining different text features could reach the best result. In the future, the classification result could be applied to developing recommender systems for topic recommendation and user prediction of online health forums.

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“…Moreover, the fact that social media intertwines so closely with people’s everyday activity makes it a vivid reflection of real life, thus presenting an intriguing data source for researchers to explore, understand, and use as the basis for making scientific discoveries. Social media allows individuals with medical concerns or problems to request, receive, and offer social support [1]. …”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Symptom Clusters Derived From Social Media and Research Study Data Using Improved $K$ -Medoid Clustering

Ping

Yang

Marshall

et al. 2016

IEEE Trans. Comput. Soc. Syst.

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Most cancer patients, including patients with breast cancer, experience multiple symptoms simultaneously while receiving active treatment. Some symptoms tend to occur together and may be related, such as hot flashes and night sweats. Co-occurring symptoms may have a multiplicative effect on patients’ functioning, mental health, and quality of life. Symptom clusters in the context of oncology were originally described as groups of three or more related symptoms. Some authors have suggested symptom clusters may have practical applications, such as the formulation of more effective therapeutic interventions that address the combined effects of symptoms rather than treating each symptom separately. Most studies that have sought to identify clusters in breast cancer survivors have relied on traditional research studies. Social media, such as online health-related forums, contain a bevy of user-generated content in the form of threads and posts, and could be used as a data source to identify and characterize symptom clusters among cancer patients. The present study seeks to determine patterns of symptom clusters in breast cancer survivors derived from both social media and research study data using improved K-Medoid clustering. A total of 50,426 publicly available messages were collected from Medhelp.com and 653 questionnaires were collected as part of a research study. The network of symptoms built from social media was sparse compared to that of the research study data, making the social media data easier to partition. The proposed revised K-Medoid clustering helps to improve the clustering performance by re-assigning some of the negative-ASW (average silhouette width) symptoms to other clusters after initial K-Medoid clustering. This retains an overall non-decreasing ASW and avoids the problem of trapping in local optima. The overall ASW, individual ASW, and improved interpretation of the final clustering solution suggest improvement. The clustering results suggest that some symptom clusters are consistent across social media data and clinical data, such as gastrointestinal (GI) related symptoms, menopausal symptoms, mood-change symptoms, cognitive impairment and pain-related symptoms. We recommend an integrative approach taking advantage of both data sources. Social media data could provide context for the interpretation of clustering results derived from research study data, while research study data could compensate for the risk of lower precision and recall found using social media data.

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Social Support and Exchange Patterns in an Online Smoking Cessation Intervention Program

Cited by 21 publications

References 22 publications

Social Support and User Engagement in Online Health Communities

Social Support and User Engagement in Online Health Communities

Classifying User Intention and Social Support Types in Online Healthcare Discussions

Breast Cancer Symptom Clusters Derived From Social Media and Research Study Data Using Improved $K$ -Medoid Clustering

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