Jul 2026· Information Hiding· pp. 1-6· 0 citations· 42 references
Computer Science
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.
Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languages remains largely unexplored. To address this gap, we curate 625 authentic mental health cases from three complementary sources: (1) publicly available Facebook posts...
Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman et al.· arXiv.org· 0 citations
Large Language Models (LLMs) are increasingly used for emotional support, yet their conversational behaviors often diverge from professional therapeutic standards. Rather than evaluating diagnostic accuracy, we assess how well these LLMs align with supportive conversational practices in digital mental well-being contex...
Alexander Marrapese, Basem Suleiman, Jinglin Sun et al.· International Conference on...· 0 citations
Evidence that automated response-level and user-perceived empathy can dissociate is shown, and a case for multi-level evaluation strategies—response-level, perceptual, and affective—when assessing empathic conversational systems is made.
Arndt Bieberstein, B. Schnitzer, Stefano Gampe et al.· International Conference on...· 1 citation
A systematic review of empirical studies reveals that empathy is highly situated and driven by functional goals, like health and well-being, transactional service, social interaction, and learning support, and a disproportionate focus on text-based over voice-based interfaces.
Supriya Khadka, Smit Desai· International Conference on...· 0 citations
This qualitative comparative study examines how ChatGPT-4o simulates empathy when responding to emotionally charged English in an EFL context. It aims to compare artificial intelligence and human responses in emotional recognition, pragmatic tone, empathetic support, and linguistic authenticity. The study is significan...
Abdullah A. Al Fraidan, Jumana Waleed Buhaimed· Arab World English Journal· 0 citations
The results revealed that while verbal and multimodal empathy significantly increased reply length, communicative acts were largely shaped by conversational context, with Sensitive context triggering more question-asking and Mental Health context leading to heightened concerns, assertive responses, and unprompted infor...
Xi Zheng, Xuyu Yang, Can Liu et al.· 0 citations
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