Predicting online news popularity based on machine learning

Min Jen Tsai*, You Qing Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Due to its fast transmission and easy accessibility features, the Internet has replaced traditional newspapers and magazines as the main channel for delivering public news. Hence, predicting the popularity of Internet news has become an essential topic. This research is based on a UCI dataset, the primary source of which is Mashable News, one of the major blogs in the world. The number of shared articles is used as a predictor of the popularity of the news, and the four types of machine learning algorithms utilized are Random Forest, LightGBM, XGBoost, and One-Class SVM. The best prediction method is One-Class SVM with 88% accuracy. This result indicates that combining Autoencoder and One-Class algorithm will optimize the prediction while detecting anomalies within imbalanced data.

Original languageEnglish
Article number108198
JournalComputers and Electrical Engineering
Volume102
DOIs
StatePublished - Sep 2022

Keywords

  • Autoencoder
  • Internet news
  • Machine learning
  • Prediction

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