Clustering-based Machine Learning Approach For Predicting Tourism Trends From Social Media Behavior

Abstract

Digital technology has significantly transformed tourist behavior, particularly in searching for, selecting, and sharing travel experiences. Social media has become a primary source of information, influencing travel decisions through real-time recommendations and user-generated content. However, the large volume of data generated by social media presents challenges in understanding and predicting tourist behavior. This study aims to analyze tourist behavior patterns using a clustering-based machine learning approach, specifically K-Means Clustering. The research examines engagement levels on platforms such as Instagram, TikTok, and TripAdvisor to categorize tourists into three key segments: Digital-Savvy Travelers, Passive Travelers, and Conservative Travelers. The results indicate that machine learning effectively analyzes large-scale tourism data, providing valuable insights for destination marketing, personalized recommendations, and service optimization. The findings highlight the potential of machine learning to identify emerging trends, improve customer segmentation, and enhance targeted promotional strategies. Understanding these patterns enables tourism businesses to create data-driven strategies aligned with modern travel behaviors. In a broader perspective, artificial intelligence can revolutionize tourism marketing, increase customer engagement, and improve the overall travel experience.