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Predicting Alcoholism Recovery from Twitter

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10899))

Abstract

We show that social media data, in the form of Twitter profiles, can be used to automatically and accurately predict whether or not an alcoholic entering treatment will achieve and maintain sobriety. This analysis is based on a dataset of 270 Twitter (225 in the training set and 45 in the test set) users who announced that they were attending their first Alcoholics Anonymous meeting and, subsequently, their sobriety or return to drinking. Our model uses a tree-based machine learning approach to make predictions over a feature set developed from automated text analysis, social network analysis, and a quantified estimation of relevant factors identified by the addiction research community. The model correctly predicts recovery status after 90 days with 80% accuracy and ROC AUC of 0.815. We describe how this data works together to produce a model, and discuss the opportunities and challenges resulting from the ability to make these types of predictions.

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Notes

  1. 1.

    http://receptiviti.com.

  2. 2.

    Link to public repository removed for anonymous submission.

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Author information

Authors and Affiliations

  1. University of Maryland, College Park, USA

    Jennifer Golbeck

Authors
  1. Jennifer Golbeck
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Correspondence to Jennifer Golbeck .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, New York, USA

    Robert Thomson

  2. Bucknell University, Lewisburg, Pennsylvania, USA

    Christopher Dancy

  3. The Ohio State University, Columbus, Ohio, USA

    Ayaz Hyder

  4. University of Michigan–Flint, Flint, Michigan, USA

    Halil Bisgin

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Golbeck, J. (2018). Predicting Alcoholism Recovery from Twitter. 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_28

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93371-9

  • Online ISBN: 978-3-319-93372-6

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