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This research investigates the growing role of Large Language Model (LLM) chatbots in mental health support by analyzing large-scale user data from six social media platforms. Using a Value-Sensitive Design (VSD) framework, the study examines how users with different mental health conditions perceive and interact with these AI tools.

Using transmodal analysis on facial recognition and team dialogues, the study examined and compared the co-occurrences of emotion and socially-shared regulatory interactions of expert and novice medical teams.

Using machine learning techniques, the study examined the phychological underpinnings in digital social annotation that are associated with received peer acknwoledgement and learners' annotation behavior.