Predicting the Popularity of Social Curation

Abstract

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 website 1 . 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.

Characteristics of social media stories

“…Kieu et al [6] proposed a method for predicting the popularity of social curation content based on a data set from Storify. They used a machine learning approach based on curator and curation features (for example, the number of followers, the number of stories for the users, and the time that the user started using Storify) from stories.…”