Gastronomic image in the foodstagrammer’s eyes – A machine learning approach |
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| Institution: | 1. School of Tourism Management, Macao Institute for Tourism Studies, Macao, China;2. School of Hospitality Management/School of Tourism Management, Macao Institute for Tourism Studies, Macao, China;3. Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, Austria;4. Department of Tourism and Service Management, Modul University Vienna, Austria;1. School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, PR China;2. Department of Parks, Recreation and Tourism Management, North Carolina State University, 27695, Raleigh, NC, USA;3. School of Community Resources and Development, Arizona State University, 85004, Phoenix, AZ, USA;4. Hainan University-Arizona State University Joint International Tourism College, Hainan University, Hainan, PR China;5. Joint College of Ningbo University and Angre University, Ningbo, Zhejiang, 315201, PR China;1. School of Tourism Management, Sun Yat-sen University, Building 329, 135 Xingangxi Road, Guangzhou, 510275, PR China;2. Key Laboratory of Sustainable Tourism Smart Assessment Technology, Ministry of Culture and Tourism of China, Beijing, PR China;3. Mt.Huangshan Scenic Area Administrative Committee, Huangshan, Anhui, 245899, PR China;1. Marketing at the Department of Management, College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates;2. International Trade at the College of Social Sciences of Konkuk University, South Korea;1. School of Hotel and Tourism Management at the Chinese University of Hong Kong, Hong Kong;2. Department of Tourism Management at Dong-A University, South Korea;1. School of Hotel and Tourism Management ,The Hong Kong Polytechnic University ,17 Science Museum Road, TST East, Kowloon, Hong Kong, China;2. The School of Hospitality Management Pennsylvania State University 201 Mateer Building, University Park, PA, 16802, USA;3. Recreation, Park, and Tourism Management, The Pennsylvania State University 704M Ford, University Park, PA, 16802, USA |
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| Abstract: | Given the rich content that foodstagrammers, people who actively share their dining experiences using photographs and texts on social media, post, they considerably shape a destination's gastronomic image. Using big data analytics, this study examined the formation of gastronomic images from foodstagrammers' perspectives and the associated emotions. Moreover, it demonstrated the applicability of the proposed machine learning approach to evaluate both textual and pictorial content on social media. The study findings extend the current understanding of gastronomic images by identifying the underlying attributes based on the interplay of the three dimensions of food, environment, and activities. Furthermore, the results reveal specific image clusters and dimensions that arouse positive sentiments among foodstagrammers and influence users' engagement with the post. For practitioners, this study provides valuable insights into foodstagrammers' behaviors by identifying the aspects of gastronomic images that effectively arouse interest and engagement, thus promoting gastronomic destinations. |
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| Keywords: | Gastronomic image Foodstagrammer Engagement Emotion Ethnic cuisine Machine learning |
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