Analysis of the Destination Image of Malioboro Yogyakarta through Social Media Reviews - Radifa Farasyara Yuwana (LITERACY : International Scientific Journals of Social, Education, Humanities, 2025)
This study analyzes the destination image of Malioboro, Yogyakarta, through the lens of social-media reviews, which increasingly shape how tourists form expectations and evaluate their experiences. The research addresses a central problem: while traditional survey-based approaches have long dominated destination image studies, they often impose rigid categories and overlook the spontaneous, nuanced perceptions expressed online. Conversely, sentiment counts from social media can be overly simplistic, failing to explain the reasons behind positive or negative evaluations. The objective of this study is to bridge these gaps by examining user-generated content (UGC) from TripAdvisor, Google Maps, and an open-access Twitter dataset to capture both qualitative themes and basic sentiment benchmarks. A descriptive-qualitative method was applied, supported by simple quantitative summaries, to code reviews into thematic categories such as cultural authenticity and atmosphere, shopping and price/value, crowding and comfort, cleanliness and environment, and accessibility and pedestrian experience. The results show that Malioboro’s image remains strongly positive overall, with cultural vibrancy, affordability, and central accessibility being its strengths, while crowd density and localized cleanliness issues emerge as persistent challenges. These findings are consistent with prior Indonesian and international studies, while extending them by showing how UGC directly constructs image attributes. The study concludes that social-media reviews are not only reflective but also constitutive of destination image, offering timely insights for managers and policymakers to sustain competitiveness. Limitations include the exclusion of visual UGC and reliance on publicly available data; future research could integrate multimodal analysis and longitudinal designs to track evolving perceptions.