IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

Submit Research Paper
ISSN: 3108-1762 (Online)
webp (1)

By /
Plagiarism Passed
Peer reviewed
Open Access

AI-ENABLED FAKE NEWS DETECTION USING BERT LANGUAGE MODEL AND LIGHT GBM CLASSIFIER

AUTHORS:
Roshan D. Warade
Mentor
Shital S. Wagh
Affiliation
Department of Computer Engg, Matoshri College of Engg, Nashik, Maharashtra, India
DOI: 10.55041/ijsmt.v2i5.336
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE REVIEW REPORT CITE THIS ARTICLE
Abstract

The project “AI-Enabled Fake News Detection Using BERT Language Model and LightGBM Classifier” is an intelligent Android application developed to identify and classify fake news articles accurately. The system uses the BERT language model for understanding the contextual meaning of news content and a LightGBM classifier for efficient prediction of whether the news is real or false. Users can enter a news article URL, and the application extracts and analyzes the article content through WebView integration. To improve reliability, the system also uses Generative AI verification by comparing the content with trusted online news sources. The application is developed using Java/XML, Firebase Realtime Database, and Firebase Generative AI, and also includes an AI chatbot assistant and related video suggestions for better user interaction and awareness. This project provides an effective and intelligent solution to decrease the Spreading of disinformation and false news.

Keywords
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 20 2026
Article Metrics
Article Views
49
PDF Downloads
5
HOW TO CITE
APA

MLA

Chicago

Copy

Warade, R. D. (2026). AI-Enabled Fake News Detection using BERT Language Model and Light GBM Classifier. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.336

Warade, Roshan. "AI-Enabled Fake News Detection using BERT Language Model and Light GBM Classifier." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.336.

Warade, Roshan. "AI-Enabled Fake News Detection using BERT Language Model and Light GBM Classifier." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.336.

References
[1] E. Essa, A. El-Shafai, and M. Abd Elaziz, “Fake news detection based on a hybrid BERT and LightGBM models,” Complex & Intelligent Systems, vol. 9, no. 6, pp. 6581–6592, 2023.

[2] N. Raza, S. Ahmed, and M. Khan, “Enhancing fake news detection with transformer-based deep learning: A multidisciplinary approach,” PLOS ONE, vol. 20, no. 3, 2025.

[3] S. Raza and M. Ding, “Fake news detection: Comparative evaluation of BERT-like encoder-only models and autoregressive decoder-only large language models,” Knowledge and Information Systems, 2025.

[4] V. Nair and P. Kumar, “A Knowledge-Based Deep Learning Approach for Fake News Detection,” Procedia Computer Science, vol. 235, pp. 120–129, 2024.

[5] M. Q. Alnabhan, A. Hassan, and R. Ahmed, “BERTGuard: Two-Tiered Multi-Domain Fake News Detection,” Future Internet, vol. 8, no. 8, 2024.

[6] A. Saadi, K. Rahman, and T. Ali, “Enhancing Fake News Detection with Transformer Models,” Engineering, Technology & Applied Science Research, vol. 15, no. 1, 2025.

[7] M. Visweswaran, R. Kumar, and S. Prakash, “Synergistic Detection of Multimodal Fake News Leveraging Deep Learning,” Procedia Computer Science, vol. 240, pp. 560–569, 2024.

[8] H. Moalla, S. Ben Ahmed, and Y. Hamdi, “Exploring the Power of Dual Deep Learning for Fake News Detection,” Informatica, vol. 48, no. 12, 2024.

[9] F. A. Alshuwaier, M. Saleh, and A. Ibrahim, “Fake News Detection Using Machine Learning and Deep Learning: A Review,” Computers, vol. 14, no. 9, 2025.

[10] M. Q. Alnabhan, A. Hassan, and R. Ahmed, “Real-Time Fake News Detection using DeBERTa-V3 and Transformer Models,” 2026.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
A Study on the Fundamentals of Block-Chain Technology
string(19) "Pallavi Nitin Patil" Patil, P. N.
(2026)
DOI: 10.55041/ijsmt.v2i5.122
Deep Learning for Waste Classification
string(14) "Anjali Kunjwal" Kunjwal, A.
(2026)
DOI: 10.55041/ijsmt.v2i5.197
Carbon Emission Analysis for Eco-Friendly Decision Making
string(17) "Malle Sai Niteesh" Niteesh, M. S.et al.
(2026)
DOI: 10.55041/ijsmt.v2i4.321
Back to Volume 02, Issue 05
Scroll to Top