从聊天机器人到知己：一项跨文化的LLM用于情感支持的使用研究
From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support
AI总结：
本研究探讨了不同国家用户对LLM用于情感支持的接受度及影响因素，通过大规模跨文化调查发现社会经济地位是关键预测因素，并揭示了多语言提示语中用户主要寻求帮助的领域。
摘要：
大型语言模型（LLMs）不仅被用于执行任务，还作为全天候、非评判性的知己提供情感支持。然而，驱动采用的因素以及用户在不同国家对情感支持交互的感知仍不清楚。为填补这一空白，我们进行了首次大规模跨文化研究，调查了来自七个国家（美国、英国、德国、法国、西班牙、意大利和荷兰）的4641名参与者。我们的结果显示，不同国家的采用率差异显著（从20%到59%）。使用混合模型分离文化影响与人口统计特征，我们发现：25-44岁、有宗教信仰、已婚以及社会经济地位较高的人群更倾向于信任、使用和认为有好处。英语国家比大陆欧洲国家显示出更积极的感知。我们进一步收集了731个真实的多语言提示语，显示用户主要寻求帮助解决孤独、压力、关系冲突和心理健康问题。我们的发现表明，LLM的情感支持使用受复杂的社会技术景观影响，并呼吁更广泛的研究来探讨如何开发、部署和管理这些系统以确保安全和知情的访问。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
