Image Popularity Prediction in Social Media Using Sentiment and Context Features

Images in social networks share different destinies: some are going to become popular while others are going to be completely unnoticed. In this paper we propose to use visual sentiment features together with three novel context features to predict a concise popularity score of social images. Experiments on large scale datasets show the benefits of proposed features on the performance of image popularity prediction. Exploiting state-of-the-art sentiment features, we report a qualitative analysis of which sentiments seem to be related to good or poor popularity. To the best of our knowledge, this is the first work understanding specific visual sentiments that positively or negatively influence the eventual popularity of images. Categories and Subject Descriptors H.3.1 [Information Storage And Retrieval]: Content Analysis and Indexing General Terms Algorithms, Experimentation Keywords Image popularity; social networks; visual sentiment; affective computing

• Type: Journal
• Year: 2015
• Where: MM
• Authors: Francesco Gelli, Tiberio Uricchio, Marco Bertini, Alberto Del Bimbo, Shih-Fu Chang
• Keywords: MM 2015, Multimedia