Abstract
Tobacco use causes serious emotional harm among smokers and it manifests in the form of mood disorders such as depression and anxiety. The effects of smoking cessation on quality of life are well documented. However, our understanding of emotional well-being of an individual in the window of quit and relapse period to provide just in time support is quite limited. In this study, we focus on social engagement, communication attributes, and emotional landscape of successful quitters as manifested in peer interactions of an online health community for smoking cessation. Further, we employed Word Embedding techniques to analyze the content-specific communication attributes in a given quit episode at scale. Results indicate users were highly engaged after a quit. The emotional index of successful quitters highlighted the fragile and complex nature of sentiments associated with a quit episode. The behavior change techniques popular before quit were ‘goals and planning’ and ‘self-belief’ and after quit were ‘feedback and monitoring’ and ‘goals and planning’. Communication genres popular before quit were ‘family and friends’ and ‘quit readiness’, whereas focus on ‘traditions’, ‘quit progress’ and ‘quit obstacles’ was high after quit. Implications for development of real-time interventions that are mindful of emotional and informational support are discussed.
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Research reported in this publication was supported by the National Library of Medicine and National Cancer Institute of the National Institutes of Health under Award Numbers R21LM012271 and R21CA220670. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Sridharan, V., Cohen, T., Cobb, N., Myneni, S. (2018). The Portrayal of Quit Emotions: Content-Sensitive Analysis of Peer Interactions in an Online Community for Smoking Cessation. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_29
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