The widespread of fake news on social media has resulted with serious real-world impacts, mounting concerns among the global net users in the last few years. This has also drawn interest from researchers around the globe to work on deception detection mechanisms to mitigate the problem. The goal is to realize a mechanism that is automatic, robust, reliable and efficient, despite various challenges that might hamper the efforts. In this paper, we present the review on the state-of-the-art of fake news detection mechanisms on social media. We first discuss the background of the problems that are surrounding fake news and the impacts it has on the users. We further describe the definition of fake news and discuss on different deception detection approaches presented in categories such as the content-based, social context-based and hybrid-based methods. We conclude the paper with four keys of open research challenges that may guide the future research.
Machine learning algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL