A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA
Article No.: 5, Pages 1 - 35
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 present a structured and linguistic approach using hinge-loss Markov random fields (HL-MRFs) to understand recovery and relapse from AUD using social media data. We evaluate our models on AA-attending users extracted from: (i) the Twitter social network and predict recovery at two different points—90 days and 1 year after the user joins AA, respectively, and (ii) the Reddit AA recovery forums and predict whether the participating user is currently sober. The two datasets present two facets of the same underlying problem of understanding recovery and relapse in AUD users. We flesh out different characteristics in both these datasets: (i) In the Twitter dataset, we focus on the social aspect of the users and the relationship with recovery and relapse, and (ii) in the Reddit dataset, we focus on modeling the linguistic topics and dependency structure to understand users’ recovery journey. We design a unified modeling framework using HL-MRFs that takes the different characteristics of both these platforms into account. Our experiments reveal that our structured and linguistic approach is helpful in predicting recovery in users in both these datasets. We perform extensive quantitative analysis of different groups of features and dependencies among them in both datasets. The interpretable and intuitive nature of our models and analysis is helpful in making meaningful predictions and can potentially be helpful in identifying and preventing relapse early.
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Publication History
Published: 05 November 2020
Accepted: 01 September 2020
Revised: 01 September 2020
Received: 01 April 2019
Published in TWEB Volume 15, Issue 1
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