• Asia pacific journal of information systems
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• v.25 no.1
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• pp.119-141
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• 2015

With the rapid spread of social media, video-sharing social media like YouTube has emerged as a consumption and distribution channel for entertainment goods such as music videos and movie trailers. In tourism research, there has been a lot of research of how the visual media such as movies and soap operas induced tourism. However, no studies have attempted to examine the role of social media as a music consumption channel and its impact on tourism. Expanding a body of media-induced tourism, we analysed the impact of video-sharing social media on music-induced tourism with a case of Korean pop music and inbound tourism to Korea. Developing a Web-crawler, we collected YouTube users' comments data on 166 Korean pop music video clips which were released from 2009 to 2012 with over 1 million view counts. Controlling many of the determinants of tourism and analysing country-by-country impact of YouTube comments with the panel data, we found that engagement of Korean pop music video clips on YouTube is a significant predictor for the flow of inbound tourists to Korea.

• The Journal of Bigdata
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• v.5 no.1
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• pp.29-39
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• 2020

Purpose The atmosphere of respect for individual tastes that have spread throughout society has changed the consumption trend. As a result, the travel industry is also seeing customized travel as a new trend that reflects consumers' personal tastes. In particular, there is a growing interest in 'film-induced tourism', one of the areas of travel industry. We hope to satisfy the individual's motivation for traveling while watching movies with customized travel proposals, which we expect to be a catalyst for the continued development of the 'film-induced tourism industry'. Design/methodology/approach In this study, we implemented a methodology through 'OCR' of extracting and suggesting film location information that viewers want to visit. First, we extract a scene from a movie selected by a user by using 'OpenCV', a real-time image processing library. In addition, we detected the location of characters in the scene image by using 'EAST model', a deep learning-based text area detection model. The detected images are preprocessed by using 'OpenCV built-in function' to increase recognition accuracy. Finally, after converting characters in images into recognizable text using 'Tesseract', an optical character recognition engine, the 'Google Map API' returns actual location information. Significance This research is significant in that it provides personalized tourism content using fourth industrial technology, in addition to existing film tourism. This could be used in the development of film-induced tourism packages with travel agencies in the future. It also implies the possibility of being used for inflow from abroad as well as to abroad.