Artificial Intelligent Based Identification and Authentication of Fake News in Social Media Platform

In recent decades, the spread of fake news, which refers to intentionally deceptive content aimed at misleading the public, has seen a significant rise. This proliferation of false information poses a threat to cultural unity and governmental stability as it fosters political divisions and creates an atmosphere of uncertainty. With the overwhelming amount of information being disseminated through online platforms, the task of human verification has become impractical, necessitating the development and implementation of automated techniques to detect and confirm fake news accurately. One of the sophisticated tactics employed by purveyors of fake news is to manipulate readers' emotions in order to increase the popularity of their articles. This investigation utilized the probabilistic dormant semantic examination method to identify instances of fake news. The research provides a comprehensive analysis of related studies to delve into the underlying principles and concepts that have contributed to this field. Furthermore, a thorough exploration of various artificial intelligence (AI) and deep learning approaches was conducted to assess their effectiveness in detecting fake news. To facilitate this analysis, three distinct datasets were employed. Overall, this research aims to combat the detrimental effects of fake news by leveraging automated techniques to identify and address its presence in online information sources.