@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 {ramesh:wi17, title = {Multi-relational influence models for online professional networks}, booktitle = {International Conference on Web Intelligence (ICWI)}, year = {2017}, pages = {291-298}, publisher = {ACM}, organization = {ACM}, abstract = {

Professional networks are a specialized class of social networks that are particularly aimed at forming and strengthening professional connections and have become a vital component of professional success and growth. In this paper, we present a holistic model to jointly represent different heterogenous relationships between pairs of individuals, user actions and their respective propagations to characterize influence in online professional networks. Previous work on influence in social networks typically only consider a single action type in characterizing influence. Our model is capable of representing and combining different kinds of information users assimilate in the network and compute pairwise values of influence taking the different types of actions into account. We evaluate our models on data from the largest professional network, LinkedIn and show the effectiveness of the inferred influence scores in predicting user actions. We further demonstrate that modeling different user actions, node features, and edge relationships between users leads to around 20\% increase in precision at top k in predicting user actions, when compared to the current state-of-the-art model.

}, author = {Ramesh, Arti and Rodriguez, Mario and Lise Getoor} } @conference {ramesh:nipsws15, title = {Understanding Influence in Online Professional Networks}, booktitle = {NIPS Workshop on Networks in Social and Information Sciences}, year = {2015}, keywords = {HL-MRFs, influence, professional networks, social networks}, author = {Ramesh, Arti and Rodriguez, Mario and Lise Getoor} } @conference {ramesh:acl15, title = {Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums}, booktitle = {53rd Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2015}, keywords = {Discussion Forums, HL-MRFs, MOOCs, Online Courses, SRL}, author = {Ramesh, Arti and Kumar, Shachi and Foulds, James and Lise Getoor} } @conference {ramesh:aaai14, title = {Learning Latent Engagement Patterns of Students in Online Courses}, booktitle = {Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence}, year = {2014}, publisher = {AAAI Press}, organization = {AAAI Press}, keywords = {MOOC, learner engagement, probabilistic modeling, structured prediction}, author = {Ramesh, Arti and Goldwasser, Dan and Huang, Bert and Daume III, Hal and Lise Getoor} } @conference {ramesh:las13, title = {Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs}, booktitle = {ACM Conference on Learning at Scale}, series = {Annual Conference Series}, year = {2014}, publisher = {ACM}, organization = {ACM}, keywords = {MOOC, learner engagement, learning analytics, online education, probabilistic modeling, structured prediction}, author = {Ramesh, Arti and Goldwasser, Dan and Huang, Bert and Daume III, Hal and Lise Getoor} } @conference {ramesh:nipsws13, title = {Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic}, booktitle = {NIPS Workshop on Data Driven Education}, year = {2013}, author = {Ramesh, Arti and Goldwasser, Dan and Huang, Bert and Daume III, Hal and Lise Getoor} }