用大模型分析社交媒体，精准捕捉游客期待，助力个性化旅游推荐。
Semiotic Reconstruction of Destination Expectation Constructs An LLM-Driven Computational Paradigm for Social Media Tourism Analytics
- 用无监督学习从社交内容中提取旅行期待，再用问卷数据微调模型。
- 发现休闲社交期待比自然情感因素更能提升用户参与度。
- 框架可推广至消费行为研究，适合做旅游与营销优化的团队。
社交媒体兴起使用户生成内容（UGC）成为影响旅游决策的关键因素，但现有分析方法难以规模化。本研究提出双阶段大模型框架：先通过无监督学习从UGC中提取旅行期待，再结合调研数据进行有监督微调。结果表明，休闲与社交类期待对用户参与度的影响大于基础的自然与情感因素。该框架将大模型转化为期待量化的精准工具，推动旅游分析方法革新，并为体验个性化与社交旅游推广提供策略支持。其可扩展性亦适用于消费者行为研究，彰显计算社会科学在营销优化中的变革潜力。
原文摘要 · Abstract (English)
Social media's rise establishes user-generated content (UGC) as pivotal for travel decisions, yet analytical methods lack scalability. This study introduces a dual-method LLM framework: unsupervised expectation extraction from UGC paired with survey-informed supervised fine-tuning. Findings reveal leisure/social expectations drive engagement more than foundational natural/emotional factors. By establishing LLMs as precision tools for expectation quantification, we advance tourism analytics methodology and propose targeted strategies for experience personalization and social travel promotion. The framework's adaptability extends to consumer behavior research, demonstrating computational social science's transformative potential in marketing optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。