Abstract :

This Study Presents A Deep Learning-based Framework For Fake News Detection By Integrating Natural Language Processing (NLP) With A Long Short-Term Memory (LSTM) Network. The Rapid Growth Of Online News Platforms And Social Media Has Significantly Increased The Spread Of Misinformation, Posing Serious Challenges To Information Credibility And Public Trust. The Proposed Framework Utilizes NLP Techniques To Extract Meaningful Textual Representations, While The LSTM Model Captures Contextual And Sequential Dependencies Within News Articles To Distinguish Between Genuine And Fake Content. By Combining Semantic Feature Extraction With Deep Sequential Learning, The Framework Reduces The Reliance On Manual Feature Engineering And Improves The Robustness Of Text Classification. The System Is Trained And Evaluated Using A Benchmark Fake News Dataset To Validate Its Effectiveness In Identifying Misleading Content. Experimental Evaluation Demonstrates Reliable Classification Performance, Computational Efficiency, And Strong Generalization Capability. The Proposed Framework Provides A Scalable And Practical Solution For Automated Fake News Detection And Offers Significant Potential For Real-time Misinformation Monitoring And Intelligent News Verification Applications.

Published:

14-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

919-924

Section:

Articles

License:

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

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