Abstract
Reading news articles is essential and critical for understanding the local, nation-wide, and global emerging and developing events, as well as understanding the citizens' demands and critics' opinions. However, with the explosion of social media as news channels, citizens and groups of professionals share news and opinions, which has been the territory of trained journalists, adding more news to process. News often comes with multimedia objects, and suffers from integrity issues, especially with the unreliable or false claims, so-called fake news or altered or alternative facts. These quantity, diversity, and integrity pose significant challenges in the information age, not only for the decision-makers, including policymakers, business leaders but also for individual citizens. This study focuses on how the machine learning classification algorithms could help the news classifications in different categories to easily access the needed category of news and to filter out the noisy and harmful news.