Fake News Detection Using Supervised Binary Classification Techniques

With easy access and aggressive data growth via social media networks, it has become challenging to differentiate between falsified and real information. Due to the ease with which information can be communicated, the amount of information that can be misrepresented has increased exponentially. When fake material is widely disseminated, the credibility of social media networks is jeopardised. Consequently, automatically validating the source, substance, and publisher of information to classify it as false or true has become a research problem. Machine learning has been helpful in data classification but has significant limitations. This research offers multiple machine learning (ML) techniques for identifying falsified and constructed information.