A Comprehensive Analysis of Fake News Detection Models: A Systematic Literature Review and Current Challenges †

Abstract

In today’s age of social networking, web news inconsistencies have become a pressing concern. These discrepancies can mislead individuals when making important purchase decisions. Despite the existing research in this area, there is a need for more empirical and rigorous investigation into the inconsistencies reported in reviews. False reporting and disinformation on social media platforms can significantly impact societal stability and peace. Fake news is frequently disseminated on social media and can easily influence and deceive populations and governments. Many researchers are working toward distinguishing fake news from genuine news on social media platforms. The practical and timely identification of fake news can help prevent its spread. Our study focuses on how machine learning and deep learning algorithms are used to detect fraudulent data. The most fundamental and practical techniques deployed over recent years are investigated, classified, and defined in numerous datasets in an extended review model. Additionally, simulation media and recorded indicators of performance are reviewed in detail. The review, as mentioned above, provides a comprehensive analysis of key research findings, delving into pertinent issues that may impact individuals in the academic and professional realms interested in augmenting the reliability of automated FND models.