Dhanya Sridhar

Assistant Professor


Curriculum vitae


dhanya.sridhar <at> mila.quebec


DIRO

University of Montreal, Mila

F.04, 6666 Rue St. Urbain



A Structured Approach to Understanding Recovery and Relapse in AA


Journal article


Yue Zhang, Arti Ramesh, J. Golbeck, Dhanya Sridhar, L. Getoor
WWW, 2018

Semantic Scholar DBLP DOI
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APA   Click to copy
Zhang, Y., Ramesh, A., Golbeck, J., Sridhar, D., & Getoor, L. (2018). A Structured Approach to Understanding Recovery and Relapse in AA. WWW.


Chicago/Turabian   Click to copy
Zhang, Yue, Arti Ramesh, J. Golbeck, Dhanya Sridhar, and L. Getoor. “A Structured Approach to Understanding Recovery and Relapse in AA.” WWW (2018).


MLA   Click to copy
Zhang, Yue, et al. “A Structured Approach to Understanding Recovery and Relapse in AA.” WWW, 2018.


BibTeX   Click to copy

@article{yue2018a,
  title = {A Structured Approach to Understanding Recovery and Relapse in AA},
  year = {2018},
  journal = {WWW},
  author = {Zhang, Yue and Ramesh, Arti and Golbeck, J. and Sridhar, Dhanya and Getoor, L.}
}

Abstract

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we take a structured approach to understand recovery and relapse from AUD using social media data. To do so, we combine linguistic and psychological attributes of users with relational features that capture useful structure in the user interaction network. We evaluate our models on AA-attending users extracted from the Twitter social network and predict recovery at two different points---90 days and 1 year after the user joins AA, respectively. Our experiments reveal that our structured approach is helpful in predicting recovery in these users. We perform extensive quantitative analysis of different groups of features and dependencies among them. Our analysis sheds light on the role of each feature group and how they combine to predict recovery and relapse. Finally, we present a qualitative analysis of the different reasons behind users relapsing to AUD. Our models and analysis are helpful in making meaningful predictions in scenarios where only a subset of features are available and can potentially be helpful in identifying and preventing relapse early.


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