Graduation Semester and Year
Fall 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Shirin Nilizadeh
Second Advisor
Gabriela Mustata Wilson
Third Advisor
Chengkai Li
Fourth Advisor
Upendranath Chakravarthy
Abstract
Social media is now central to daily life, offering users a space to share content and opinions. However, these platforms also facilitate the spread of hate speech and misinformation, which can negatively impact public health. This dissertation develops methodologies to analyze social media data for insights that could inform health interventions. The research first examines user responses to toxic content, focusing on behavioral and emotional reactions, as well as group dynamics and bystander effects in toxic interactions. Another key focus is public opinion toward health interventions, particularly COVID-19 vaccination, using geolocated posts and analyzing factors such as race, ethnicity, and social determinants. Additionally, it explores the role of misinformation in vaccine hesitancy and considers social media as a cost-effective source for health metrics like health literacy. Key findings reveal that toxic interactions are likely to increase negative emotions and lead users to unfollow instigators. The study also observes a “bystander effect” in online settings, highlighting the potential role of social norms. Further, social media data helps identify regions that are likely to express higher vaccine hesitancy and aids in examining associations between state-level health and social composition variables with attitudes toward abortion expressed in geolocated posts. This dissertation underscores social media’s potential as a valuable data source for understanding population health dynamics.
Keywords
social media analysis, social dynamics, online toxicity, vaccine hesitancy, misinformation, public health interventions, abortion, LLM-based stance detection models
Disciplines
Applied Statistics | Artificial Intelligence and Robotics | COVID-19 | Data Science | Information Security | Maternal and Child Health | Multivariate Analysis | Other Computer Sciences | Statistical Models
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Aleksandric, Ana, "Understanding Social Dynamics in Toxic Conversations and Public Health Intervention Acceptance on Social Media" (2024). Computer Science and Engineering Dissertations. 402.
https://mavmatrix.uta.edu/cse_dissertations/402
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, COVID-19 Commons, Data Science Commons, Information Security Commons, Maternal and Child Health Commons, Multivariate Analysis Commons, Other Computer Sciences Commons, Statistical Models Commons