Abstract :

Fake News Has Emerged As A Critical Challenge In The Digital Information Ecosystem, Particularly With The Rapid Growth Of Social Media Platforms, Online News Portals, And Instant Messaging Applications. The Speed At Which Information Spreads Has Outpaced Traditional Verification Mechanisms, Allowing Misleading, False, Or Deliberately Fabricated News To Influence Public Opinion, Social Harmony, And Even Democratic Processes. Fake News Detection Using Natural Language Processing (NLP) Focuses On Leveraging Computational Linguistic Techniques And Machine Learning Models To Automatically Identify And Classify News Content As Genuine Or Fake Based On Textual Patterns, Semantics, And Contextual Cues. NLP Enables Systems To Process Large Volumes Of Unstructured Text, Extract Meaningful Features Such As Syntax, Sentiment, And Discourse Structure, And Learn Hidden Relationships That Distinguish Authentic Journalism From Deceptive Narratives. By Integrating Preprocessing, Feature Extraction, And Classification Algorithms, NLP-based Fake News Detection Systems Aim To Provide Scalable, Real-time Solutions That Reduce Human Dependency And Improve Information Credibility. This Research Emphasizes The Importance Of Linguistic Analysis, Data-driven Learning, And Automation To Combat Misinformation, Highlighting How NLP Can Act As A Powerful Tool In Ensuring Trustworthy Digital Communication And Safeguarding Society From The Harmful Impacts Of Fake News.

Published:

01-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1-7

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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