@inproceedings{amat-lefort-etal-2026-chatbots,
title = "From Chatbots to Confidants: A Cross-Cultural Study of {LLM} Adoption for Emotional Support",
author = "Amat-Lefort, Natalia and
Yazan, Mert and
Curry, Amanda Cercas and
Plaza-del-Arco, Flor Miriam",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.160/",
pages = "3668--3695",
ISBN = "979-8-89176-459-0",
abstract = "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 country-level 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 also 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. We share our code and data publicly [{\ensuremath{<}}https://confidant-ai.github.io/LLMs-emotional-support/{\ensuremath{>}}]."
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<abstract>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 country-level 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 also 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. We share our code and data publicly [\ensuremath<https://confidant-ai.github.io/LLMs-emotional-support/\ensuremath>].</abstract>
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%0 Conference Proceedings
%T From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support
%A Amat-Lefort, Natalia
%A Yazan, Mert
%A Curry, Amanda Cercas
%A Plaza-del-Arco, Flor Miriam
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F amat-lefort-etal-2026-chatbots
%X 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 country-level 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 also 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. We share our code and data publicly [\ensuremath<https://confidant-ai.github.io/LLMs-emotional-support/\ensuremath>].
%U https://aclanthology.org/2026.emnlp-main.160/
%P 3668-3695
Markdown (Informal)
[From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support](https://aclanthology.org/2026.emnlp-main.160/) (Amat-Lefort et al., EMNLP 2026)
ACL