An Attention-based Neural Model for Popularity Prediction in Social Service

Abstract

Web based social services like Twitter and Weibo are popular nowadays. A large number of users generate massive text-based information continuously and as a result, many topic popularity prediction models arise. However, most of them simply use forwarding or view count to measure popularity, ignoring semantic relations between posts and the topic. Moreover, few model use the important component of opinion - sentiment, to enhance topic popularity prediction. Therefore, in this paper, we propose a attention-based convolutional network model to generate sentiment-aware time series and predict topic popularity. Our model quantifies popularity more accurately with posts' semantic information and incorporates sentiment into popularity prediction. Concretely, our model is composed of a semantic-aware popularity quantification metric, a sentiment intensity detection neural network, and an Autoregressive convolutional network prediction scheme. We have conducted extensive experiments to prove that our model outperforms the existing popularity prediction models on a real-world Twitter dataset. We find that sentiment time series have the ability to improve popularity prediction accuracy significantly. Furthermore, we find our prediction scheme is superior to other models in utilizing sentiment information.