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Exploring the power of multimodal features for predicting the popularity of social media image in a tourist destination
- Title
- Exploring the power of multimodal features for predicting the popularity of social media image in a tourist destination
- Authors
- Gupta V.; Jung K.; Yoo S.-C.
- Ewha Authors
- 유승철
- SCOPUS Author ID
- 유승철
- Issue Date
- 2020
- Journal Title
- Multimodal Technologies and Interaction
- ISSN
- 2414-4088
- Citation
- Multimodal Technologies and Interaction vol. 4, no. 3, pp. 1 - 23
- Keywords
- Artificial intelligence; Content management system; Strategic communication
- Publisher
- MDPI AG
- Indexed
- SCOPUS
- Document Type
- Article
- Abstract
- Social media platforms are widely used nowadays by various businesses to promote their products and services through multimedia content. Instagram is one of those platforms, which is used not only by companies to promote their products but also by local governments to promote tourist destinations. Predicting the popularity of the promotional tourist destination images helps marketers to plan strategically. However, given the abundance of images posted on Instagram daily, identifying the factors that determine the popularity of an image is a big challenge, due to informal and noisy visual content, frequent content evolution, a lack of explicit visual elements, and people’s informal behavior in liking, commenting on, and viewing the images. We present an approach to identify the factors most responsible for the popularity of tourist destinations-related images on Instagram. Our approach provides a proof of concept for an artificial intelligence (AI)-based real-time content management system, which will help to promote a tourist destination. The experiments on a collection of posts crawled from the official Instagram account of Jeju Island, which is one of the most popular tourist destinations in Korea, show that the recency of the post is the most important predictor of the number of likes and comments it will receive. Moreover, the combination of visual content and context features is an excellent predictor of popularity. The number of likes and comments are found to be complementary to each other for predicting image popularity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- DOI
- 10.3390/mti4030064
- Appears in Collections:
- 사회과학대학 > 커뮤니케이션·미디어학전공 > Journal papers
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