This study examines the hierarchical structure of financial needs as articulated in social media discourse, employing generative AI techniques to analyze large-scale textual data. While human needs encompass a broad spectrum from fundamental survival to psychological fulfillment financial needs are particularly critical, influencing both individual well-being and day-to-day decision-making. Our research advances the understanding of financial behavior by utilizing large language models (LLMs) to extract and analyze expressions of financial needs from social media posts. We hypothesize that financial needs are organized hierarchically, progressing from short-term essentials to long-term aspirations, consistent with theoretical frameworks established in the behavioral sciences. Through computational analysis, we demonstrate the feasibility of identifying these needs and validate the presence of a hierarchical structure within them. In addition to confirming this structure, our findings provide novel insights into the content and themes of financial discussions online. By inferring underlying needs from naturally occurring language, this approach offers a scalable and data-driven alternative to conventional survey methodologies, enabling a more dynamic and nuanced understanding of financial behavior in real-world contexts.

翻译：本研究采用生成式人工智能技术分析大规模文本数据，探讨社交媒体话语中表达的财务需求层次结构。人类需求涵盖从基本生存到心理满足的广泛谱系，其中财务需求尤为关键，既影响个体福祉，也作用于日常决策。我们的研究通过利用大语言模型（LLMs）从社交媒体帖子中提取并分析财务需求表达，推进了对财务行为的理解。我们假设财务需求呈现层次化组织特征，从短期必需品延伸到长期发展目标，这与行为科学领域既有的理论框架相一致。通过计算分析，我们论证了识别这些需求的可行性，并验证了其中存在的层次结构。除确认该结构外，我们的研究结果还为在线财务讨论的内容与主题提供了新的见解。通过从自然语言中推断潜在需求，该方法为传统调查方法提供了可扩展且数据驱动的替代方案，从而能在现实情境中对财务行为实现更具动态性与细微差异的理解。