Open Access

ARTICLE

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

Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹

1 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
2 School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China

* Corresponding Author: Yongbin Wang. Email:

(This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2023, 76(2), 1621-1641. https://doi.org/10.32604/cmc.2023.040095

Received 04 March 2023; Accepted 19 May 2023; Issue published 30 August 2023

Abstract

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.

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Keywords

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