Comparative Analysis of Fake Comments Posted in Twitter Using Different Machine Learning Models

The introduction of the World Wide Web and the fast assumption of online social media and the entertainment stages (like Facebook, Twitter, LinkedIn, etc.) prepared for data dissemination that has never been seen in the mankind's set of experiences earlier. Customers are creating and exchanging more data than at any other time in recent memory thanks to the continued use of virtual entertainment platforms, some of which are delusional and have no application in the real world. It is a difficult task to classify a written article as false or misleading using a computer. Without a doubt, before making a determination on the veracity of an article, even a specialist in a given field needs to consider numerous points of view. In this work, we suggest using a machine learning approach for content article classification that is automated. Differentiate the real from the phony. Using those characteristics, we train a variety of alternative machine learning estimation methods using diverse ensemble procedures and evaluate their performance on datasets. The exploratory assessment supports our suggested ensemble learner approach's higher-level presentation as compared to individual learners.