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Retweet prediction is a challenging problem in social media sites (SMS). Inthis paper, we study the problem of image retweet prediction in social media,which predicts the image sharing behavior that the user reposts the imagetweets from their followees. Unlike previous studies, we learn user preferenceranking model from their past retweeted image tweets in SMS. We first proposeheterogeneous image retweet modeling network (IRM) that exploits users' pastretweeted image tweets with associated contexts, their following relations inSMS and preference of their followees. We then develop a novel attentionalmulti-faceted ranking network learning framework with textually guidedmulti-modal neural networks for the proposed heterogenous IRM network to learnthe joint image tweet representations and user preference representations forprediction task. The extensive experiments on a large-scale dataset fromTwitter site shows that our method achieves better performance than otherstate-of-the-art solutions to the problem.

Textually Guided Ranking Network for Attentional Image Retweet Modeling