Classification of worldwide news articles by perceived quality, 2018-2024

TL;DR

This study evaluates machine learning and deep learning models on a large dataset to classify news articles by perceived quality, demonstrating high accuracy especially with models like ModernBERT-large.

Contribution

It introduces a new dataset of over 1.4 million news articles with expert-rated quality labels and compares multiple models for quality classification.

Findings

Deep learning models outperform traditional classifiers in accuracy.

ModernBERT-large achieves the highest accuracy of 87.44%.

Traditional classifiers like Random Forest reach 73.55% accuracy.

Abstract

This study explored whether supervised machine learning and deep learning models can effectively distinguish perceived lower-quality news articles from perceived higher-quality news articles. 3 machine learning classifiers and 3 deep learning models were assessed using a newly created dataset of 1,412,272 English news articles from the Common Crawl over 2018-2024. Expert consensus ratings on 579 source websites were split at the median, creating perceived low and high-quality classes of about 706,000 articles each, with 194 linguistic features per website-level labelled article. Traditional machine learning classifiers such as the Random Forest demonstrated capable performance (0.7355 accuracy, 0.8131 ROC AUC). For deep learning, ModernBERT-large (256 context length) achieved the best performance (0.8744 accuracy; 0.9593 ROC-AUC; 0.8739 F1), followed by DistilBERT-base (512 context length) at 0.8685 accuracy and 0.9554 ROC-AUC. DistilBERT-base (256 context length) reached 0.8478 accuracy and 0.9407 ROC-AUC, while ModernBERT-base (256 context length) attained 0.8569 accuracy and 0.9470 ROC-AUC. These results suggest that the perceived quality of worldwide news articles can be effectively differentiated by traditional CPU-based machine learning classifiers and deep learning classifiers.