A Probabilistic Approach to Modeling Socio-Behavioral Interactions

The vast growth and reach of internet and social media have led to a tremendous increase in socio-behavioral interaction content on the web. The ever-increasing number of online interactions have led to a growing interest to understand and interpret online communications to enhance user experience. This includes personalization, user retention, predicting user interests, and product recommendations. In this thesis, I address how to use machine learning methods to model sociobehavioral interactions and predict user behavior patterns in online networks. In the first part of this proposal, I focus on one such emerging online interaction platform—online courses (MOOCs). Structured data from these courses contain behavioral, and interaction data and provide opportunity to design machine learning methods for understanding user interaction. The data also contains unstructured data, such as natural language text from forum posts and other online discussions. I present a family of probabilistic models that I have developed for: 1) modeling student engagement, 2) predicting student completion and dropouts, 3) modeling student sentiment toward various course aspects (e.g., content vs. logistics), and 4) detecting coarse and fine-grained course aspects (e.g., grading, video, content) in online courses. These methods have the potential to improve student experience and focus limited instructor resources in ways that will have the most impact. In the second part of the proposal, I describe how I plan to extend the above-mentioned models to model socio-behavioral interactions at multiple scales in networks. I plan to test the effectiveness of this model via experimentation on different types of platforms such as MOOCs and professional networks (e.g., LinkedIn).