Tourist Sites and Visitor Numbers in Taiwan: An Online Buzz Analysis

This study highlights the influence of online buzz by examining the effect of tourism and recreational site-related keywords on inbound tourism to Taiwan. Using large-scale, real-time data from Google Trends, we assess how search interest in key attractions predicts tourist arrivals. Principal Component Analysis (PCA) is applied to construct a composite index of search activity, allowing us to explore its relationship with inbound tourist numbers. The results indicate that six keywords - Longshan Temple, Jiufen, Miramar Entertainment Park, Fushoushan Farm, Yangmingshan National Park, and Raohe Street Night Market - are significantly associated with tourist arrivals. The growing use of social media and real-time information platforms has enhanced tourists’ awareness and interest in Taiwanese attractions, supporting the steady growth of inbound tourism. Government initiatives promoting smart tourism through digital technology and big data further amplify the visibility of Taiwan’s tourism resources and contribute to tourism development. The study’s principal contribution lies in leveraging real-time data; by providing richer and timelier information than conventional official statistics, these data yield more precise and statistically significant results. Moreover, because few studies use real-time data to examine the direct effect of online buzz on inbound tourist arrivals to Taiwan, this paper fills that gap. JEL classification: C60, O11, R11. Keywords: Principal Component Analysis (PCA), Tourism and Recreational Site, Vector Autoregressive (VAR).