From Scripted Responses To Therapeutic Dialogue: A Linguistic And Human Values Analysis Of Mental Health Chatbots

Abstract

Mental health (MH) chatbots are increasingly used to provide accessible, on-demand emotional support, yet it remains unclear how these systems linguistically construct and communicate care. This work-in-progress examines whether MH chatbots produce responses that reflect supportive value orientations and counseling-adjacent tone. We conduct an observational analysis of responses from three widely used MH chatbots (Wysa, Sintelly, and Youper) across context-aware scenario prompts and a standardized-question session. Responses are analyzed using the SemEval’23 “Adam Smith” human value detection model and LIWC’22 psycholinguistic measures, including Language Style Matching (LSM), Clout, and Authenticity. Values such as “Security: Personal” and “Benevolence: Caring” appear consistently across systems, with contextual variation in secondary value emphasis. Linguistic patterns show moderate-to-high LSM and consistently high Clout, with Authenticity varying by scenario. These findings are exploratory signals intended to inform future evaluation and design of supportive conversational mental health systems.