Comparative Study of Random Forest Algorithm and Logistic Regression in the Analysis of Fake News

The onset of the World Wide Web and the dramatic rise of social networking sites, including Facebook and Twitter, have aided an unparalleled delivery of content throughout humanity's history. Consumers today generate and share more information than ever before. However, the present use of social media platforms, which is often misleading and has no relevance to reality, undermines the reliability of the information available. Automated categorization of a content editorial as false info or psyops presents a complex contest. Even an expert in a particular field must consider multiple aspects before passing judgement on an article's veracity. This study presents by using a machine learning forms a partnership to classify media articles auto. This study explores numerous different texts assets which can discern between good and what is bad product. Practicing a combination of different machine learning techniques employing numerous different classification techniques and try to assess their success on four real-world datastores feature. The explored different of our envisioned as a technical beginner attitude proves its hegemony over leaners. The rapid increase of knowledge on social media and its free entry and exit have made it challenging to tell apart between correct and incorrect knowledge. The ease for sharing information has contributed to the proliferation of falsified information, jeopardizing the credibility of social media networks. As a result, a research challenge has emerged to automatically verify information's authenticity based on its source, content, and publisher using a variety of text classifiers. While machine learning has been useful in information classification, it has some limitations. This study examines it many computational methods, such like Logistic Regression and Random Forest, for characterizing falsify and faked information. The limitations of these methods and approaches are discussed, along with potential ways to improve them.