• Introduces parameter-efficient LLM framework achieving SOTA performance with 9 × faster processing. • Reveals heterogeneous platform-specific quality patterns through spatio-temporal-semantic analysis. • Identifies three behavioral signatures of high-reliability users via heterogeneous network analysis. • Enables platform-specific interventions to boost content reliability and personalization. The rapid growth of tourist-generated content demands scalable and reliable quality assessment methods. This study introduces an LLM-driven framework that combines parameter-efficient fine-tuning and prompt engineering to evaluate content quality accurately and interpretably. Applied to 484,930 reviews from MaFengWo, TripAdvisor, and Ctrip, the approach achieves superior performance (RMSE=0.3040, NDCG@100=0.500, BERTScore=77.95%) with 9 × higher efficiency. Spatial-temporal-semantic analyses reveal platform-specific quality patterns: MaFengWo exhibits prominent spatial centrality and stable temporal cointegration; TripAdvisor demonstrates simplified core-periphery structures with high volatility; Ctrip presents dynamic multicentricity particularly in Shanghai. Two domestic platforms, MaFengWo and Ctrip, expose systematic deficiency on the theme ‘ Decision-making Plan ’ (92.9∼96.4% lacking operational suggestions), while international TripAdvisor emphasizes ‘ Practical Information ’ and ‘ Consumption Activity ’ but 40.76% neglects original viewpoints. Heterogeneous network analysis identifies the behavioral signatures of high-reliability user—preference attachment, quality stability, and profile homogeneity. This work bridges theoretical rigor with operational scalability, demonstrating the potential of LLMs in content governance for digital tourism.
Gao et al. (Sat,) studied this question.