人们如何使用 ChatGPT：来自印度、尼日利亚、巴西和巴基斯坦的对话级证据
How People Use ChatGPT: Conversation-Level Evidence from India, Nigeria, Brazil, and Pakistan
AI总结：
本研究基于印度、尼日利亚、巴西和巴基斯坦1,252名用户的202,590次ChatGPT对话，揭示个人使用占主导（55-64%），并发现各国独特的本地化用途，强调对话级、国家敏感的测量对理解AI采用的重要性。
AI中文摘要：
公众对人们如何使用基于LLM的对话式AI助手的理解，主要来自OpenAI和Anthropic发布的聚合平台报告，这些报告对数亿用户应用固定分类法和推断的人口统计信息，且仅发布外部研究者无法重新分析的汇总统计。我们提供了一种互补的、对话级的视角：完整的ChatGPT导出数据，包含来自印度、尼日利亚、巴西和巴基斯坦的1,252名用户的202,590次对话，并配有自我报告的年龄和性别，时间跨度从2022年12月到2026年2月。据我们所知，这是首个跨多个非西方国家的、基于对话级且具有人口统计基础的ChatGPT使用比较。我们利用平台自身的分类器、无监督主题发现以及对表达性对话的主题分析，探讨这些用户使用ChatGPT的目的（用途）、谈论的内容（主题）以及互动方式（交互模式）。在每个国家，个人使用占对话的55-64%，课程作业与工作一样常见，因此工作场所生产力仅描述了少数使用情况。无监督主题发现揭示了OpenAI分类法归入通用类别的国家特定用途：印度和巴西的健康与保健、巴基斯坦的乌尔都语-英语翻译、尼日利亚的时事、尼日利亚和巴基斯坦的宗教问题，以及巴西的自我反思。在三年间，寻求信息的对话比例仅略有下降，委派任务的对话比例并未增长，而用户表达自我的对话在每个国家从几个百分点上升到约五分之一或更多。因此，同一产品在每个国家与不同的本地需求相关联，理解采用的含义需要对话级、国家敏感的测量以及全球聚合数据。
英文摘要：
Public understanding of how people use LLM-based conversational AI assistants comes primarily from aggregate platform reports by OpenAI and Anthropic, which apply fixed taxonomies and inferred demographics to hundreds of millions of users and release only summary statistics that outside researchers cannot re-analyze. We provide a complementary, conversation-level view: complete ChatGPT exports comprising 202,590 conversations from 1,252 users across India, Nigeria, Brazil, and Pakistan, paired with self-reported age and gender and spanning December 2022 to February 2026. To our knowledge this is the first conversation-level, demographically grounded comparison of ChatGPT use across multiple non-Western countries. We ask what these users use ChatGPT for (purpose), what they talk about (topics), and how they engage with it (mode of interaction), using the platform's own classifiers, unsupervised topic discovery, and a thematic analysis of expressive conversations. Personal use accounts for 55-64% of conversations in every country and coursework is about as common as work, so workplace productivity describes a minority of use. Unsupervised topic discovery surfaces country-specific uses that the OpenAI taxonomy folds into generic categories: health and wellness in India and Brazil, Urdu-English translation in Pakistan, current affairs in Nigeria, religious questions in Nigeria and Pakistan, and self-reflection in Brazil. Over three years, the share of conversations that seek information declined only modestly and the share that delegate a task did not grow, while conversations in which users express themselves rose from a few percent to roughly a fifth or more in every country. The same product is thus attached to different local needs in each country, and understanding what adoption means requires conversation-level, country-sensitive measurement alongside global aggregates.
