Abstract
Smoking is one of the leading causes of preventable death, being responsible for about six million deaths annually worldwide. Most smokers want to quit, but many find quitting difficult. The Internet enables people interested in quitting smoking to connect with others via online communities; however, the characteristics of these discussions are not well understood. This work aims to explore the textual cues of an online community interested in quitting smoking: www.reddit.com/r/stopsmoking – “a place for redditors to motivate each other to quit smoking”. A total of approximately 5,000 posts were randomly selected from the community. Four subgroups of posts based on the cessation days of abstainers were defined: S0: within the first week, S1: within the first month (excluding cohort S0), S2: from second month to one year, and S3: beyond one year. Psycho-linguistic features and content topics were extracted from the posts and analysed. Machine learning techniques were used to discriminate the online conversations in the first week S0 from the other subgroups. Topics and psycho-linguistic features were found to be highly valid predictors of the subgroups, possibly providing an important step in understanding social media and its use in studies of smoking and other addictions in online settings.
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Notes
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The list of all topics can be accessed via http://bit.ly/21Z0o4r.
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Acknowledgment
This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning.
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Nguyen, T., Borland, R., Yearwood, J., Yong, HH., Venkatesh, S., Phung, D. (2016). Discriminative Cues for Different Stages of Smoking Cessation in Online Community. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_12
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DOI: https://doi.org/10.1007/978-3-319-48743-4_12
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