@conference {323, title = {A Structured Approach to Understanding Recovery and Relapse in AA}, booktitle = {The Web Conference (WWW)}, year = {2018}, 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{\textemdash}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 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.

}, url = {https://github.com/yzhan202/zhang-www18-experiments}, author = {Zhang, Yue and Ramesh, Arti and Golbeck, Jennifer and Dhanya Sridhar and Lise Getoor} } @conference {zheleva:snakdd08, title = {Using Friendship Ties and Family Circles for Link Prediction}, booktitle = {2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD)}, year = {2008}, author = {Zheleva, Elena and Lise Getoor and Golbeck, Jennifer and Kuter, Ugur} }