In-Depth Analysis of Fake News Detection Strategies

Abstract

Considering the proliferation of fake news as a result of the rise of digital media and the widespread dissemination of information on line, this issue has become a significant concern for society. This survey article offers an extensive summary of the cutting-edge methods used to identify fake news. We examine two main types of detection techniques: content-based techrüques, which examine the textual and contextual aspects of news stories, and social context techniques, which make use of user actions and social network dynamics. The benefits and drawbacks of the current approaches and talk about how NLP, machine learning, and neural network analysis can improve and automate detection initiatives are discussed in this paper. The article concludes with a discussion of the open research questions, which are based on an exhaustive examination throughout the work. Keywords: Social Media Fake News Detection NLP ML Content-based techniques_x000D_ Introduction_x000D_ The phenomenon of fake news is now recognized as an enormous obstacle in this period, which is characterized by the prevalence of digital communication and the quick distribution of information. It poses enormous dangers to the integrity of information ecosystems and to the expression of public opinion. Through the