A Comprehensive Survey on Automatic Detection of Fake News Using Natural Language Processing: Challenges and Limitations

The study examines how Natural Language Processing (NLP) can be used to automatically detect fake news and how it can be applied to fact-checking in the disciplines of linguistics, computer science, journalism, and information sciences. By evaluating the efficacy, dependability, and breadth of various NLP algorithms, this study demonstrates the possibilities and limitations of autonomous fake news identification. The importance of striking a balance between the technical form of information and its social-cognitive dimensions was revealed by the study. An overemphasis on the technical components can lead to fragmented comprehension, so it is essential to strike a balance between the technical form of information and its social-cognitive dimensions. In the age of digital self-publishing, the importance of authoritativeness in determining the credibility of information has also been raised as a significant concern. The report emphasizes the need for an integrative approach to prevent the spread of fake news, recommending interdisciplinary collaboration and the ongoing refinement of NLP research methods for future studies.