Abstract
ABSTRACT Social media platforms have a significant role in determining destination image. This study examines the impact of length of stay on destination image as reflected on visitors’ posts on social media through a comparative analysis of day trippers and tourists. Two text mining methods (Term Frequency-Inverse Document Frequency and Latent Dirichlet Allocation) and an image classification model (ResNet) were applied to Weibo data to identify day trippers’ and tourists’ online destination image. Differences were found between day trippers and tourists when examining keywords, topics, and visual scenes. The keyword-based image was predominantly utilitarian for day trippers and more recreational-oriented for tourists. Moreover, tourists shared a greater variety of topic-based image attributes (e.g. accommodation, catering, activity) than day trippers, and form an abundant holistic image. Both groups were highly consistent in the visual category of common images and top-10 visual scenes of unique features, though significant differences were recorded when considering gender.
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