From online engagement to offline health outcomes—analyzing user behaviors in online health communities
Creators: name: Wang, Xiangyu
Publisher: name: University of Iowa
Year: 2023
Subjects: public abstract

description: The prevalence of the internet provides a great lens to study “Information-TechnologyPeople” interactions. The global healthcare system is undergoing a radical shift as steadily more patients use the internet to take control of their personal well-being. Online health communities (OHCs) have become a major resource for people with similar health concerns to get together. To better understand the dynamics of OHCs, promote successful online communities, and improve individual health via interventions, the main strand of my thesis addresses two closely related and complementary research questions: 1) how to foster continued online engagement for users in OHCs, and 2) how to better support OHC users’ pursuits of better health outcomes in their offline lives.
description: To answer the first research question, I investigate how the user’s first experience seeking support affects their early-stage engagement in OHCs. Taking advantage of text mining and machine learning techniques, posts have been classified according to the type of social support the user was seeking or providing to others. This study focuses on new users whose first posts were to seek support. Results reveal that the amount of received support and how well it fits the users’ needs are positive and significant predictors of their continued engagement. The findings suggest that increasing the chances of a first-time support seeker receiving the type of support they initially sought can help online communities better retain new users.
description: In addition to examining users’ first-time online experiences, looking at their experiences over time periods of various lengths (e.g., long-term hiatus, or user churn) can also be an important predictor of users’ future engagement. The second piece of my thesis expands the analysis from new users to include all users and looks at their behaviors as well. Specifically, the study proposes a general and flexible framework to predict user posting activities. We train a Long Short-Term v Memory (LSTM) model using the trajectories of two user behaviors: posting volume and content. Through various experiments, results demonstrate that our proposed approach outperforms baseline methods. Such predictions can help OHC managers improve user retention via interventions such as email reminders. I also investigate which discussion topics are the most sensitive to possible interventions. The findings suggest that discussions about companionship and emotional support can have a strong effect on predicting user posting activities.
description: The second piece of my dissertation explores the connection between OHC users’ online support activities and their offline health outcomes. When promoting user online participation, a useful practice is to recruit a core group of active users (i.e., seed users) to create new content and encourage user participation. Using data from an OHC for smoking cessation, I find that support from seed users predicts member abstinence. In addition, seed users who have successful experience in quitting can better help others. Content analysis also reveals differences between the support provided by seed users who were former smokers vs. current smokers. Outcomes of this study can aid the design of a supportive OHC and the promotion of smoking cessation.