Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model's performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The typical convolution neural network gets headline semantic representation for modeling news headlines. In addition, a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement. A factorization machine is employed to model the interaction relationship between meta-features. Considering the role of these features at different stages, the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically. During the training phase, this work designs a loss function based on Newton's cooling law to train the model better. Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.