Artificial News Popularity Detection Based on Telegram Channels in Azerbaijan

Abstract

With the exponential growth of digital media, readers face a daunting task of sifting through vast amounts of information to identify important news. This problem is especially critical for media professionals, journalists, and news agencies who need to quickly filter news articles to identify relevant and significant stories. Machine learning models offer a promising solution by automatically classifying news articles based on their significance. In this paper, we propose novel machine learning models for news significance detection, leveraging state-of-the-art deep learning architectures and a dataset of news articles. We evaluate our models using a variety of performance metrics and demonstrate their effectiveness compared to existing methods. Our proposed approach has the potential to significantly improve the efficiency and accuracy of news selection, benefiting both media professionals and readers alike. Furthermore, it can be beneficial to forecast the popularity of fake news and prevent its dissemination in society. Approximately, 2800 Azerbaijani news articles have been collected from telegram and labeled as popular or unpopular according to statistical calculation results. For news popularity detection, application of SVM, Random Forest and Neural network models and their results have been discussed in this paper.