mlsanityData to 7 Oct 2026

From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support

arXiv 2604.25525 ↗First posted 28 Apr 2026cs.CL cs.HCcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional support. Yet what drives adoption and how users perceive emotional support interactions across countries remains unknown. To address this gap, we present the first large-scale cross-cultural study of LLM use for emotional support, surveying 4,641 participants across seven countries (USA, UK, Germany, France, Spain, Italy, and The Netherlands). Our results show that adoption rates vary dramatically across countries (from 20% to 59%). Using mixed models that separate cultural effects from demographic composition, we find that: Being aged 25-44, religious, married, and of higher socioeconomic status are predictors of positive perceptions (trust, usage, perceived benefits), with socioeconomic status being the strongest. English-speaking countries consistently show more positive perceptions than Continental European countries. We further collect a corpus of 731 real multilingual prompts from user interactions, showing that users mainly seek help for loneliness, stress, relationship conflicts, and mental health struggles. Our findings reveal that LLM emotional support use is shaped by a complex sociotechnical landscape and call for a broader research agenda examining how these systems can be developed, deployed, and governed to ensure safe and informed access.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 39 references that are in the map. First posted on arXiv 28 Apr 2026.

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References · 9

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  3. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  4. 2024Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directionscited by 26
  5. 2025From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversationscited by 23
  6. 2024Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMscited by 19
  7. 2025The AI Gap: How Socioeconomic Status Affects Language Technology Interactionscited by 15
  8. 2022Computer says "No": The Case Against Empathetic Conversational AIcited by 9
  9. 2025Language Models Predict Empathy Gaps Between Social In-groups and Out-groupscited by 6

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