Predicting online news popularity based on machine learning. (September 2022)

• Record Type: Journal Article
• Authors: Tsai, Min-Jen; Wu, You-Qing
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Machine learning -- Internet news -- Prediction -- Autoencoder; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108198
• Languages: English
• ISSNs: 0045-7906

Abstract

Research highlights: A UCI online news popularity dataset was utilized in this study, and the quality of the dataset was improved after using data pre-processing methods such as normalization and principal component analysis. The number of shares was transformed into a popularity or unpopularity classification problem. The experimental results show that using a single-class classification algorithm is better than a two-class classification algorithm when the data is unbalanced. The contribution of this paper is to modify the classification boundaries from 1400 to 50, 000 in order to form imblanced data distributions and attempt to combine an Autoencoder and One-Class SVM algorithms to overcome the problem of imbalanced data. 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. Graphical abstract: One-class SVM algorithm based on an autoencoder adopted in this study outperforms other algorithms, namely Random Forest, XGBoost, and LightGBM, in the category of the accuracy, the precision, the recall, and F1 scores. Image, graphical abstract