Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1312 2018 2018 Department of Computer Science, Zagazig university, Sharqiyah, Egypt Nehal Nehal Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt Ibrahim El .. Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt Ahmed Sleem In recent years, spreading social media platforms and mobile devices led to more social data, advertisements, political opinions, and celebrity news proliferating fake news. Fake news can cause harm to networks, communications, and users and cause trust issues toward government, healthcare, or social media platforms. This inspired many researchers to implement models to detect falsified information content. But there are still many issues that need to be discussed and explored. In our paper, we introduce categories of fake news detection methods and compare these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of social media. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in social networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks to aid the readers and researchers in explaining the motive and role of the different machine learning fusion paradigms to offer them a comprehensive realization of unexplored issues related to false information and other scenarios of social networks. 2021 2021 41 57 10.54216/FPA.040105 https://www.americaspg.com/articleinfo/3/show/1312