Fake news detection through machine learning and deep learning by Yogita IIngale and Shraddha Pathare

Social media has become a dominant platform where individuals freely express their opinions and share information widely and rapidly. While it serves as a convenient and accessible source of news and communication, it also poses significant risks due to the widespread circulation of “fake news.” The rapid dissemination of misinformation can mislead the public, cause societal unrest, and influence major events, such as political elections. This growing concern has drawn attention from both the public and governmental organizations, as the manipulation of information can significantly affect public opinion and decision-making processes. Consequently, the detection of fake news on social media has emerged as a critical area of research. Identifying misinformation presents numerous challenges due to its complexity and the evolving tactics of those spreading it. This paper explores various contemporary approaches to fake news detection, focusing on the implementation of machine learning and deep learning techniques to enhance detection accuracy and reliability.