Extracting Key Challenges in Achieving Sobriety Through Shared Subspace Learning

  • Haripriya HarikumarEmail author
  • Thin Nguyen
  • Santu Rana
  • Sunil Gupta
  • Ramachandra Kaimal
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)


Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance.


Social media Reddit Shared subspace Topics Alcohol addiction 



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


  1. 1.
    Abbar, S., Mejova, Y., Weber, I.: You tweet what you eat: Studying food consumption through Twitter. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3197–3206 (2015)Google Scholar
  2. 2.
    Cunha, T.O., Weber, I., Haddadi, H., Pappa, G.L.: The effect of social feedback in a reddit weight loss community. In: Proceedings of the 6th International Conference on Digital Health Conference, pp. 99–103 (2016)Google Scholar
  3. 3.
    De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., Kumar, M.: Discovering shifts to suicidal ideation from mental health content in social media. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2098–2110 (2016)Google Scholar
  4. 4.
    Ekpenyong, N.S., Aakpege, N.Y.: Alcohol consumption pattern and risky behaviour: a study of university of port harcourt. IOSR J. Humanit. Soc. Sci. (IOSR-JHSS) 19(3), 1 (2014)Google Scholar
  5. 5.
    Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York (2007)Google Scholar
  6. 6.
    Gilpin, E.A., Pierce, J.P., Farkas, A.J.: Duration of smoking abstinence and success in quitting. J. Nat. Cancer Inst. 89(8), 572 (1997)CrossRefGoogle Scholar
  7. 7.
    Gupta, S.K., Phung, D., Adams, B., Tran, T., Venkatesh, S.: Nonnegative shared subspace learning and its application to social media retrieval. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1169–1178 (2010)Google Scholar
  8. 8.
    Gupta, S.K., Phung, D., Adams, B., Venkatesh, S.: Regularized nonnegative shared subspace learning. Data Min. Knowl. Discov. 26(1), 57–97 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kelly, J.F., Hoeppner, B., Stout, R.L., Pagano, M.: Determining the relative importance of the mechanisms of behavior change within Alcoholics Anonymous: A multiple mediator analysis. Addiction 107(2), 289–299 (2012)CrossRefGoogle Scholar
  10. 10.
    Magura, S., McKean, J., Kosten, S., Tonigan, J.S.: A novel application of propensity score matching to estimate Alcoholics Anonymous effect on drinking outcomes. Drug Alcohol Depend. 129(1), 54–59 (2013)CrossRefGoogle Scholar
  11. 11.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Tamersoy, A., De Choudhury, M., Chau, D.H.: characterizing smoking and drinking abstinence from social media. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp. 139–148 (2015)Google Scholar
  13. 13.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B (Methodological) 58, 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Weitzman, E.R.: Poor mental health, depression, and associations with alcohol consumption, harm, and abuse in a national sample of young adults in college. J. Nerv. Mental Dis. 192(4), 269–277 (2004)CrossRefGoogle Scholar
  15. 15.
    Witteman, J., Post, H., Tarvainen, M., de Bruijn, A., Perna, E.D.S.F., Ramaekers, J.G., Wiers, R.W.: Cue reactivity and its relation to craving and relapse in alcohol dependence: A combined laboratory and field study. Psychopharmacology 232(20), 3685–3696 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Haripriya Harikumar
    • 1
    Email author
  • Thin Nguyen
    • 1
  • Santu Rana
    • 1
  • Sunil Gupta
    • 1
  • Ramachandra Kaimal
    • 2
  • Svetha Venkatesh
    • 1
  1. 1.Centre for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia
  2. 2.Computer Science and Engineering DepartmentAmrita UniversityKollamIndia

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