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Understanding Behavioral Differences Between Short and Long-Term Drinking Abstainers from Social Media

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Advanced Data Mining and Applications (ADMA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

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Abstract

Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence.

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Notes

  1. 1.

    http://www.cdc.gov/features/alcoholconsumption/.

  2. 2.

    http://healthland.time.com/2012/03/07/quitting-drugs-or-alcohol-10-of-the-u-s- population-has-done-it/.

  3. 3.

    http://redditmetrics.com/history.

  4. 4.

    http://redditmetrics.com/visuals/reddit-growth-gif.

  5. 5.

    https://www.reddit.com/r/stopdrinking.

  6. 6.

    https://praw.readthedocs.io/en/stable/.

  7. 7.

    http://redditmetrics.com/r/stopdrinking.

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Acknowledgment

This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning.

Author information

Authors and Affiliations

  1. Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Australia

    Haripriya Harikumar, Thin Nguyen, Sunil Gupta, Santu Rana & Svetha Venkatesh

  2. Computer Science and Engineering Department, Amrita University, Kollam, India

    Ramachandra Kaimal

Authors
  1. Haripriya Harikumar
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  2. Thin Nguyen
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  3. Sunil Gupta
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  4. Santu Rana
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  5. Ramachandra Kaimal
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  6. Svetha Venkatesh
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Corresponding author

Correspondence to Haripriya Harikumar .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

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© 2016 Springer International Publishing AG

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Harikumar, H., Nguyen, T., Gupta, S., Rana, S., Kaimal, R., Venkatesh, S. (2016). Understanding Behavioral Differences Between Short and Long-Term Drinking Abstainers from Social Media. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_36

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  • DOI: https://doi.org/10.1007/978-3-319-49586-6_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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