Semiotic Reconstruction of Destination Expectation Constructs An LLM-Driven Computational Paradigm for Social Media Tourism Analytics
This work addresses scalability issues in tourism analytics for researchers and marketers, offering targeted strategies for personalization and promotion, though it is incremental in applying LLMs to a specific domain.
The study tackled the problem of analyzing user-generated content for tourism decisions by introducing a dual-method LLM framework, finding that leisure and social expectations drive engagement more than natural or emotional factors, with LLMs established as precision tools for expectation quantification.
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.