Abstract
The increasing use of Instagram as a platform to share travel experiences has led to a vast amount of visual content related to destination images. This study proposes a machine learning approach to cluster destination images on Instagram. The objective is to identify the underlying patterns and themes in travel images shared on Instagram, which could provide useful insights for the tourism industry. The study uses a dataset of 10,000 Instagram images with destination tags and applies a deep learning approach to extract visual features from the images. K-means clustering is then applied to group images based on visual similarities. The results show that machine learning techniques can be used to cluster destination images on Instagram into meaningful categories such as natural landscapes, cultural landmarks, food, and cityscapes. These insights can be used to develop targeted marketing strategies and improve tourism experiences.
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