An Analysis and Detection of Misleading Information on Social Media Using Machine Learning Techniques

Abstract

The widespread adoption of user-generated material on social networking sites enables the gathering of individuals. The internet has grown in popularity based on multidisciplinary information sources. Nowadays, every individual has constantly bombarded the internet with information, and it is very challenging for every person to distinguish between factual and misleading information. Social networking sites mainly rely on content providers to filter the information. The chapter has focused on political news where the machine learning-based hybrid approach has been used to detect false statements. The work is to determine the information is deceptive or accurate. The authors investigate the link between publisher attitude and news stance, and the hyperpartisan media sources are more prone than other resources to propagate false information. Furthermore, they show that this is not required to examine news and information to recognize misleading headlines, but that utilizing variables such as publisher bias, user interactions, and news-related pictures may obtain equivalent results.