A framework to predict early news popularity using deep temporal propagation patterns

作者：

Highlights：

• A corpus containing data from cybersecurity news websites and Twitter is created.

• A model based on news temporal propagation patterns is proposed to predict its popularity.

• Content features, user features, and news source features are also used.

• A novel deep learning model is devised to predict early news popularity.

摘要

•A corpus containing data from cybersecurity news websites and Twitter is created.•A model based on news temporal propagation patterns is proposed to predict its popularity.•Content features, user features, and news source features are also used.•A novel deep learning model is devised to predict early news popularity.

论文关键词：Popularity,Long short-term memory,Temporal propagation patterns,Convolutional neural network

论文评审过程：Received 18 August 2020, Revised 25 October 2021, Accepted 31 December 2021, Available online 15 January 2022, Version of Record 1 February 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116496