Distinguishing Truth from Deception: Employing Machine Learning for Rumor Verification

One of the most significant problems in the Internet and social media world we live in today is fake news. The fact that news can travel great distances in a matter of hours is a blessing, but it is also upsetting to witness several individuals and organizations disseminating false information. Many false news reports feature propaganda against an individual, group, organization, or political party. There are too many phoney news stories for one human to handle. Therefore, it is necessary to have machine learning classifiers that can recognize these bogus news reports automatically. In this study data of fake news collected from kaggle and different approaches applied into it like Logistic regression, hard voting classifier, graded boosting, adaboost and random forest approach. This will help any one to make right decision and analyze the truthiness involved into the news. Applying such algorithms techniques will make the environment peaceful and also help to reduce riots among different religions or communities, which is very often in many countries. These techniques will also be helpful in making the readers or viewers smart by simply not relying on any news blindly but also to use smart techniques by which they can able to decide about the percentage of truthiness in any news on social media platform.