Research Article
Mining Influential Factors and Spatio-Temporal Patterns of Travel Intention Based on Social Media Data
@INPROCEEDINGS{10.4108/eai.27-10-2023.2341916, author={Hang Zhao}, title={Mining Influential Factors and Spatio-Temporal Patterns of Travel Intention Based on Social Media Data}, proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China}, publisher={EAI}, proceedings_a={ICEMBDA}, year={2024}, month={1}, keywords={lda tf-idf travel intention spatial autocorrelation}, doi={10.4108/eai.27-10-2023.2341916} }- Hang Zhao
Year: 2024
Mining Influential Factors and Spatio-Temporal Patterns of Travel Intention Based on Social Media Data
ICEMBDA
EAI
DOI: 10.4108/eai.27-10-2023.2341916
Abstract
The questionnaire method is becoming obsolete. Social media data contains textual, location and temporal information, making it possible to more effectively discover the factors influencing large-scale public intention to travel and to conduct spatio-temporal and thematic analysis. Based on Sina Weibo data posted by users who wanted to visit Xinjiang throughout 2022, we used latent Dirichlet allocation(LDA) models, term frequency–inverse document frequency(TF-IDF) algorithms, spatial autocorrelation and other theories and techniques to mine the data, and obtained the following conclusions:we got seven topics,and beautiful view was the factor with the highest proportion;tourists were driven by anticipation in the early period (January-May), and by specific things about the tourist destination in the peak period (June-August), and in the late period (September-December) had resistance susceptibility;focus on gourmet food was stronger in the west and south, and beautiful view was stronger in the more economically developed regions.