DeepBlue: Bi-Layered LSTM for Tweet popUlarity Estimation

摘要

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this article, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity and (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate these two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a united LSTM model. We also design a content attention mechanism to consider different impacts of historical tweets in terms of content similarity. We then propose a time aware mechanism to address the time interval irregularity issue. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.