Topic-Specific Retweet Count Ranking for Weibo

In this paper, we study topic-specific retweet count ranking problem in Weibo. Two challenges make this task nontrivial. Firstly, traditional methods cannot derive effective feature for tweets, because in topic-specific setting, tweets usually have too many shared contents to distinguish them. We propose a LSTM-embedded autoencoder to generate tweet features with the insight that any different prefixes of tweet text is a possible distinctive feature. Secondly, it is critical to fully catch the meaning of topic in topic-specific setting, but Weibo can provide little information about topic. We leverage real-time news information from Toutiao to enrich the meaning of topic, as more than 85% topics are headline news. We evaluate the proposed components based on ablation methods, and compare the overall solution with a recently-proposed tensor factorization model. Extensive experiments on real Weibo data show the effectiveness and flexibility of our methods.