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.

中文翻译：

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理解 AA 康复和复发的结构化和语言方法

酒精中毒，也称为酒精使用障碍 (AUD)，是影响全球数百万人的严重问题。众所周知，从 AUD 中恢复具有挑战性，并且在参加诸如匿名戒酒会 (AA) 等康复计划后，通常会导致不同时间点的复发。在这项工作中，我们提出了一种结构化的语言方法，使用铰链损失马尔可夫随机场 (HL-MRF) 来了解使用社交媒体数据从 AUD 中恢复和复发。我们评估我们的 AA 参与用户模型，这些模型来自：(i) Twitter 社交网络并预测两个不同时间点的恢复情况——分别在用户加入 AA 后 90 天和 1 年，以及 (ii) Reddit AA 恢复论坛和预测参与用户当前是否清醒。这两个数据集呈现了理解 AUD 用户恢复和复发的同一潜在问题的两个方面。我们在这两个数据集中充实了不同的特征：（i）在 Twitter 数据集中，我们关注用户的社交方面以及与恢复和复发的关系，（ii）在 Reddit 数据集中，我们专注于对语言进行建模主题和依赖结构，以了解用户的恢复过程。我们使用 HL-MRF 设计了一个统一的建模框架，该框架考虑了这两个平台的不同特征。我们的实验表明，我们的结构化和语言方法有助于预测这两个数据集中用户的恢复情况。我们对两个数据集中的不同特征组和它们之间的依赖关系进行了广泛的定量分析。