A Review and Analysis on Fake News Detection Based on Artificial Intelligence and Data Science

Abstract

Along with the development of the Internet, the emergence and widespread adoption of the social media concept have changed the way news is formed and published. News has become faster, less costly and easily accessible with social media. This change has come along with some disadvantages as well. In particular, beguiling content, such as fake news made by social media users, is becoming increasingly dangerous. The fake news problem, despite being introduced for the first time very recently, has become an important research topic due to the high content of social media. Writing fake comments and news on social media is easy for users. This study attempts to investigate advanced and state-ofthe-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. The objective of this study was to synthesize the current possibilities of using AI to combat disinformation. Disinformation is currently a growing problem, and it is increasingly important to understand how we can utilize AI to help address this issue. Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) and Natural Language processing (NLP) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies this study made a review analysis by considering the authors their own perceptions in the previous years was presented in this paper.