An Analysis of City Image by Exploiting Social Media: Toward a Deeper Understanding of Multiple Characteristics and Their Temporal Changes Using Machine Learning

Since successful city branding plays a crucial role in establishing a city’s competitiveness and uniqueness, cities worldwide are actively involved in shaping their city images. City images are multifaceted and dynamic, influenced not only by geographic, symbolic, and cultural elements individually, but also by the intricate interplay among diverse components that constitute a city. In addition, the advent of mobile devices and the rise of social media have intensified the trend of creating new city images while some existing ones fade away. However, traditional survey-based analytical methods struggle to accurately capture the complexity and temporal changes in city images. Although some researchers have explored the analysis of city images using machine learning and social media data, there is still a gap in research that comprehensively captures the intricate nature of city images, encompassing diverse themes that evolve over time. This article aims to provide a deeper understanding of the multiple characteristics and their temporal changes in city images by leveraging geographic information from social media posts. Through an extensive analysis of three popular tourist destinations in South Korea—Gongju, Gyeongju, and Jeju—we uncover the interconnections, transformations, and underlying reasons behind actual city images. Specifically, our findings reveal that Gongju is evolving from a historically focused city into a multifaceted image incorporating welfare, business, and experiential activities. Gyeongju is shifting from a traditional cultural tourism destination to one increasingly centered on leisure and culinary experiences. Jeju Island, once dominated by traditional themes such as tangerine farming and women divers, is now characterized by modern attractions such as the Aewol café street, reflecting its transition into a premier leisure destination. Furthermore, our study contributes to the validation and enrichment of existing knowledge on dynamically evolving city images by highlighting the similarities and differences between our findings and those derived from conventional survey-based approaches.