Textually Guided Ranking Network for Attentional Image Retweet Modeling
This addresses the problem of predicting image sharing behavior for social media users, but it is incremental as it builds on existing retweet prediction methods with a new network design.
The paper tackles image retweet prediction in social media by learning user preference ranking from past retweeted image tweets, using a novel attentional multi-faceted ranking network with textually guided multi-modal neural networks. The method achieves better performance than state-of-the-art solutions on a large-scale Twitter dataset.
Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees. Unlike previous studies, we learn user preference ranking model from their past retweeted image tweets in SMS. We first propose heterogeneous image retweet modeling network (IRM) that exploits users' past retweeted image tweets with associated contexts, their following relations in SMS and preference of their followees. We then develop a novel attentional multi-faceted ranking network learning framework with textually guided multi-modal neural networks for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task. The extensive experiments on a large-scale dataset from Twitter site shows that our method achieves better performance than other state-of-the-art solutions to the problem.