Semiotic Reconstruction of Destination Expectation Constructs An LLM-Driven Computational Paradigm for Social Media Tourism Analytics

TL;DR

This paper presents a novel LLM-based framework for analyzing social media content to extract travel expectations, improving scalability and precision in tourism analytics and enabling personalized marketing strategies.

Contribution

It introduces a dual-method approach combining unsupervised and supervised LLM techniques for expectation extraction from UGC, advancing computational social science in tourism.

Findings

Leisure and social expectations significantly influence engagement.

LLMs effectively quantify expectations from social media data.

The framework is adaptable to broader consumer behavior research.

Abstract

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.