Abstract
The amount and variety of social media content such as status, images, movies, and music are increasing rapidly. Accordingly, the social curation service is emerging as a new way to connect, select, and organize information on a massive scale. One noticeable feature of social curation services is that they are loosely supervised: the content that users create in the service is manually collected, selected, and maintained. A large proportion of these contents are arbitrarily created by inexperienced users. In this paper, we look into social curation, particularly, the Storify website1. This is the most popular social curation for creating stories included in various domains such as Twitter, Flicker, and YouTube.We propose a machine learning method with feature extraction to filter these contents and to predict the popularity of social curation data.
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Kieu, B.T., Ichise, R., Pham, S.B. (2015). Predicting the Popularity of Social Curation. In: Nguyen, VH., Le, AC., Huynh, VN. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-11680-8_33
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DOI: https://doi.org/10.1007/978-3-319-11680-8_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11679-2
Online ISBN: 978-3-319-11680-8
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