Deeply Exploit Visual and Language Information for Social Media Popularity Prediction
Jianmin Wu, Liming Zhao, Dangwei Li, Chen-Wei Xie, Siyang Sun, Yun Zheng
Abstract
Social media popularity prediction task is to predict future attractiveness of new posts, which could be applied for online advertising, social recommendation, and demand prediction. Existing methods have explored multiple feature types to model the popularity prediction, including user profile, tag, space-time, category, and others. However, images and texts of social media posts, as important and primary information, are usually used by simple or insufficient processing. In this paper, we propose a method to deeply exploit visual and language information to explore the attractiveness of posts. Specifically, images are parsed from multiple perspectives including multi-modal semantic representation, perceptual image quality, and scene analysis. Different word-level and sentence-level semantic embedding are extracted from all available language texts including title, tags, concept and category. It makes social media popularity modeling more reliable with the powerful visual and language representation. Experimental results demonstrate the effectiveness of exploiting visual and language information by the proposed method, and we achieve new state-of-the-art results on the SMP Challenge at ACM Multimedia 2022.