• M. Lakshmi Prasad
• Ch.Srinivasulu
• V Alekhya
• J.Vaishnavi
• B.Sree Sony

The abundance of fake news, also known as deep fakes, misinformation, disinformation, and post-truth, has raised concerns about the operation of the Internet and social media in contemporary democracies. Digital dishonesty affects individuals, society, and the economy because of how quickly and widely it is disseminated. The nation's security could also be at peril. The provenance and traceability of data are ensured by blockchain and other distributed ledger technologies (DLTs), which create a peer-to-peer secure platform for information storage and exchange and offer a transparent, unalterable, and verifiable record of transactions. This overview examines how DLTs might be used to avoid digital fraud by identifying the most pertinent applications and the most significant unresolved issues.

Text analysis, Natural language processing, Machine learning, Text classification, Authorship identification, Source identification, Text mining, Data analysis, Linguistic patterns

"UnMasking Misleading Information: Exploring a Natural Language Processing Approach for Fake News Detection and Authorship Attribution", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e593-e600, November-2023, Available :http://www.jetir.org/papers/JETIR2311481.pdf

"UnMasking Misleading Information: Exploring a Natural Language Processing Approach for Fake News Detection and Authorship Attribution", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe593-e600, November-2023, Available at : http://www.jetir.org/papers/JETIR2311481.pdf