Abstract: Anxiety disorders are characterized by persistent and excessive form of fear and worry that interferes with daily functioning, distinguishing it from the adaptive anxiety that helps individuals respond to challenges. Despite affecting millions worldwide and costing a significant public health burden, anxiety disorders still remain underdiagnosed than actual prevalence due to lack of understanding and stigmatization. Leveraging machine learning (ML) and natural language processing (NLP) approaches can help bridge this gap by enabling scalable and accessible mental health assessments, offering a data-driven understanding of anxiety from individual and societal perspectives, and shedding light on societal stigmas toward mental health conditions. At the same time, advancing ML and NLP techniques for anxiety research presents unique technical challenges, such as effectively modeling linguistic markers of anxiety and ensuring interpretability in mental health predictions.
This dissertation investigates anxiety from both individual and societal perspectives using artificial intelligence. First, we explore individual manifestations of anxiety through three methodological advancements: (1) integrating contextual and discourse-level embeddings to improve language-based anxiety prediction using Facebook posts and selfreported surveys; (2) enhancing cognitive dissonance detection in Twitter dataset with transfer learning and active learning; and (3) developing longitudinal representation learning approaches that achieve both predictive utility and interpretability of adolescent psychopathology. Finally, we extended our analysis to societal dimension of anxiety by identifying and categorizing social norms expressed in Reddit and Twitter posts and examining their associations with anxiety. By combining data-driven methods with psychological insights, this work studies anxiety from various angles - capturing both individual experiences and societal influences - offering a step toward a more comprehensive understanding of its causes and manifestations.
Speaker: Swanie Juhng
https://stonybrook.zoom.us/j/
This dissertation investigates anxiety from both individual and societal perspectives using artificial intelligence. First, we explore individual manifestations of anxiety through three methodological advancements: (1) integrating contextual and discourse-level embeddings to improve language-based anxiety prediction using Facebook posts and selfreported surveys; (2) enhancing cognitive dissonance detection in Twitter dataset with transfer learning and active learning; and (3) developing longitudinal representation learning approaches that achieve both predictive utility and interpretability of adolescent psychopathology. Finally, we extended our analysis to societal dimension of anxiety by identifying and categorizing social norms expressed in Reddit and Twitter posts and examining their associations with anxiety. By combining data-driven methods with psychological insights, this work studies anxiety from various angles - capturing both individual experiences and societal influences - offering a step toward a more comprehensive understanding of its causes and manifestations.
Speaker: Swanie Juhng
https://stonybrook.zoom.us/j/