Classifying Fake News Article through NLP

The rise of deepfakes, misinformation, disinformation, and the post-truth era—collectively referred to as fake news—has raised serious concerns about the role of the Internet and social media in shaping public opinion in modern democratic societies. This project aims to address the threat of digital deception by exploring the use of Natural Language Processing (NLP) techniques for the classification of fake news articles. In parallel, it investigates how Blockchain and other Distributed Ledger Technologies (DLTs) can enhance the provenance, traceability, and integrity of digital information. These technologies offer a transparent, immutable, and verifiable record of transactions, creating a peer-to-peer secure platform for storing and exchanging data. The study provides an overview of the most relevant applications of DLTs in combating fake news and identifies key open challenges in the field. Furthermore, it offers recommendations for future research to strengthen resilience against cyber threats and enhance trust in today’s online media ecosystems.