Social Popularity Score

Abstract

In this paper, we propose an algorithm to predict the social popularity (i.e., the numbers of views, comments, and favorites) of content on social networking services using only text annotations. Instead of analyzing image/video content, we try to estimate social popularity by a combination of weight vectors obtained from a support vector regression (SVR) and tag frequency. Since our proposed algorithm uses text annotations instead of image/video features, its computational cost is small. As a result, we can estimate social popularity more efficiently than previously proposed methods. Furthermore, tags that significantly affect social popularity can be extracted using our algorithm. Our experiments involved using one million photos on the social networking website Flickr, and the results showed a high correlation between actual social popularity and the determination thereof using our algorithm. Moreover, the proposed algorithm can achieve high classification accuracy with regard to a classification between popular and unpopular content.