Abstract
Predicting news popularity is an essential topic in the news industry. It is challenging because numerous factors influence public response to the news. This paper presents \(F^4\), a neural model to predict news popularity by learning news embedding from global, local, long-term and short-term factors. \(F^4\) integrates a sentence encoding module to represent the local context of each news story; a heterogeneous graph-based module to capture the short-term information propagation from current buzz words to each news story; a sequential module to extract long-term popularity features in entity sequence; and an attention module to learn global news-entity correlations. Extensive experiments on real-world Chinese and English news datasets demonstrated that \(F^4\) outperforms state-of-the-art baselines in predicting and ranking news popularity.
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Acknowledgements
Chen Lin is the corresponding author. Chen Lin is supported by the Natural Science Foundation of China (No. 61972328), Joint Innovation Research Program of Fujian Province China (No.2020R0130). Hui Li is supported by the Natural Science Foundation of China (No. 62002303), Natural Science Foundation of Fujian Province China (No. 2020J05001). Quan Zou is supported by Natural Science Foundation of China (No. 61922020).
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Fan, S., Lin, C., Li, H., Zou, Q. (2021). News Popularity Prediction with Local-Global Long-Short-Term Embedding. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_6
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