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Original Article
Deep Learning-Based Approach for Fake News Detection Using LSTM and Bi-LSTM Models
Tetlapu Guna Sri Manga Bhavani1
Suneel Kumar Duvvuri2
1 Student, M.Sc (Computer Science), Government College (Autonomous), Rajahmundry, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 445-453
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602063
References
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10. P. Durga Prasad, P. Deepthi Sri, D. P. Kumar, S. A. Alisha, V. S. Manikanta, and S. K. Duvvuri, “Comparative Analysis of Machine
Learning Algorithms for Email Spam Detection using TF-IDF,” International Scientific Journal of Engineering and Management, vol. 05,
no. 04, pp. 1–9, Apr. 2026, doi: 10.55041/ISJEM06439.
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17. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews
Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi:
10.32628/IJSRSET261371.
18. D. P. Kadupu, D. P. Patinavalasa, and D. Suneel Kumar, “Deep Learning-Based Sentiment Analysis of Hotel Reviews Using LSTM and
Bidirectional LSTM Models,” Indian Journal of Computer Science and Technology, p. 262, Apr. 2026, doi:
10.59256/indjcst.20260501038.
19. M. Suja, P. Lavanya, P. D. Prasad, and S. K. Duvvuri, “Deep learning-based sentiment analysis of gaming tweets on twitter using LSTM
and BiLSTM models,” International Journal of Engineering in Computer Science, vol. 8, no. 1, pp. 215–222, Jan. 2026, doi:
10.33545/26633582.2026.v8.i1b.269.
20. H. Ahmed, I. Traore, and S. Saad, “Detecting Opinion Spams and Fake News Using Text Classification,” Security and Privacy, vol. 1,
no. 1, p. e9, 2018, doi: 10.1002/spy2.9.
21. K. Shu, S. Wang, and H. Liu, “Understanding User Profiles on Social Media for Fake News Detection,” IEEE Conference on Multimedia
Information Processing, pp. 430–435, 2019, doi: 10.1109/MIPR.2019.00092.
22. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” ArXiv, 2014, [Online]. Available:
https://arxiv.org/pdf/1408.5882.pdf
23. J. Ma, W. Gao, and K.-F. Wong, “Detecting Rumors from Microblogs with Recurrent Neural Networks,” in Proceedings of the 25th
International Joint Conference on Artificial Intelligence (IJCAI), 2016, pp. 3818–3824. [Online]. Available: https://aclanthology.org/P16-
2034.pdf
24. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, [Online]. Available:
https://www.bioinf.jku.at/publications/older/2604.pdf
25. X. Zhang, “Fake News Detection using BiLSTM,” ArXiv, 2020, [Online]. Available: https://arxiv.org/pdf/2001.09457.pdf
26. [Y. Liu, “Hybrid CNN-LSTM Model,” IEEE, 2018, [Online]. Available:
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8487078
27. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical Attention Networks for Document Classification,” Proceedings
of NAACL-HLT, pp. 1480–1489, 2016, doi: 10.18653/v1/N16-1174.
28. J. Devlin, “BERT: Pre-training of Deep Bidirectional Transformers,” ArXiv, 2018, [Online]. Available:
https://arxiv.org/pdf/1810.04805.pdf
29. R. Kaliyar, “Fake News Detection using BiLSTM,” Applied Intelligence, 2021, [Online]. Available:
https://link.springer.com/content/pdf/10.1007/s10489-020-01787-2.pdf
30. S. Singhania, N. Fernandez, and S. Rao, “3HAN: A Deep Neural Network for Fake News Detection,” International Conference on Neural
Information Processing, pp. 572–581, 2017, doi: 10.1007/978-3-319-70096-0_60.
31. J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, “FEVER: A Large-Scale Dataset for Fact Extraction and Verification,” in
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT),
2018, pp. 809–819. doi: 10.18653/v1/N18-1074.
211–236, 2017, doi: 10.1257/jep.31.2.211.
2. S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science (1979)., vol. 359, no. 6380, pp. 1146–1151, 2018,
doi: 10.1126/science.aap9559.
3. K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake News Detection on Social Media: A Data Mining Perspective,” ACM SIGKDD
Explorations, vol. 19, no. 1, pp. 22–36, 2017, doi: 10.1145/3137597.3137600.
4. D. et al. Lazer, “The science of fake news,” Science (1979)., vol. 359, no. 6380, pp. 1094–1096, 2018, doi: 10.1126/science.aao2998.
5. C. Castillo, M. Mendoza, and B. Poblete, “Information credibility on Twitter,” in Proceedings of the 20th International Conference on
World Wide Web, 2011, pp. 675–684. doi: 10.1145/1963405.1963500.
6. G. Pennycook and D. G. Rand, “Fighting misinformation on social media using crowdsourced judgments of news source quality,”
Proceedings of the National Academy of Sciences, vol. 116, no. 7, pp. 2521–2526, 2019, doi: 10.1073/pnas.1806781116.
7. X. Zhou and R. Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Comput. Surv.,
vol. 53, no. 5, pp. 1–40, 2020, doi: 10.1145/3395046.
8. N. J. Conroy, V. L. Rubin, and Y. Chen, “Automatic deception detection: Methods for finding fake news,” Proceedings of the Association
for Information Science and Technology, vol. 52, no. 1, pp. 1–4, 2015, doi: 10.1002/pra2.2015.145052010082.
9. I. Rish, “An Empirical Study of the Naive Bayes Classifier,” in IJCAI Workshop on Empirical Methods in AI, 2001, pp. 41–46.
10. P. Durga Prasad, P. Deepthi Sri, D. P. Kumar, S. A. Alisha, V. S. Manikanta, and S. K. Duvvuri, “Comparative Analysis of Machine
Learning Algorithms for Email Spam Detection using TF-IDF,” International Scientific Journal of Engineering and Management, vol. 05,
no. 04, pp. 1–9, Apr. 2026, doi: 10.55041/ISJEM06439.
11. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
12. J. L. Elman, “Finding Structure in Time,” Cogn. Sci., vol. 14, no. 2, pp. 179–211, 1990, doi: 10.1207/s15516709cog1402_1.
13. Y. Bengio, P. Simard, and P. Frasconi, “Learning Long-Term Dependencies with Gradient Descent is Difficult,” IEEE Trans. Neural
Netw., vol. 5, no. 2, pp. 157–166, 1994, doi: 10.1109/72.279181.
14. S. K. DUVVURI, Applications of Artificial Intelligence across Domains. Commissionerate of Collegiate Education, Government of
Andhra Pradesh, 2026. doi: 10.5281/zenodo.18623057.15. M. Schuster and K. K. Paliwal, “Bidirectional Recurrent Neural Networks,” IEEE Transactions on Signal Processing, vol. 45, no. 11, pp.
2673–2681, 1997, doi: 10.1109/78.650093.
16. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal of Recent Trends in Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
17. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews
Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi:
10.32628/IJSRSET261371.
18. D. P. Kadupu, D. P. Patinavalasa, and D. Suneel Kumar, “Deep Learning-Based Sentiment Analysis of Hotel Reviews Using LSTM and
Bidirectional LSTM Models,” Indian Journal of Computer Science and Technology, p. 262, Apr. 2026, doi:
10.59256/indjcst.20260501038.
19. M. Suja, P. Lavanya, P. D. Prasad, and S. K. Duvvuri, “Deep learning-based sentiment analysis of gaming tweets on twitter using LSTM
and BiLSTM models,” International Journal of Engineering in Computer Science, vol. 8, no. 1, pp. 215–222, Jan. 2026, doi:
10.33545/26633582.2026.v8.i1b.269.
20. H. Ahmed, I. Traore, and S. Saad, “Detecting Opinion Spams and Fake News Using Text Classification,” Security and Privacy, vol. 1,
no. 1, p. e9, 2018, doi: 10.1002/spy2.9.
21. K. Shu, S. Wang, and H. Liu, “Understanding User Profiles on Social Media for Fake News Detection,” IEEE Conference on Multimedia
Information Processing, pp. 430–435, 2019, doi: 10.1109/MIPR.2019.00092.
22. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” ArXiv, 2014, [Online]. Available:
https://arxiv.org/pdf/1408.5882.pdf
23. J. Ma, W. Gao, and K.-F. Wong, “Detecting Rumors from Microblogs with Recurrent Neural Networks,” in Proceedings of the 25th
International Joint Conference on Artificial Intelligence (IJCAI), 2016, pp. 3818–3824. [Online]. Available: https://aclanthology.org/P16-
2034.pdf
24. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, [Online]. Available:
https://www.bioinf.jku.at/publications/older/2604.pdf
25. X. Zhang, “Fake News Detection using BiLSTM,” ArXiv, 2020, [Online]. Available: https://arxiv.org/pdf/2001.09457.pdf
26. [Y. Liu, “Hybrid CNN-LSTM Model,” IEEE, 2018, [Online]. Available:
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8487078
27. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical Attention Networks for Document Classification,” Proceedings
of NAACL-HLT, pp. 1480–1489, 2016, doi: 10.18653/v1/N16-1174.
28. J. Devlin, “BERT: Pre-training of Deep Bidirectional Transformers,” ArXiv, 2018, [Online]. Available:
https://arxiv.org/pdf/1810.04805.pdf
29. R. Kaliyar, “Fake News Detection using BiLSTM,” Applied Intelligence, 2021, [Online]. Available:
https://link.springer.com/content/pdf/10.1007/s10489-020-01787-2.pdf
30. S. Singhania, N. Fernandez, and S. Rao, “3HAN: A Deep Neural Network for Fake News Detection,” International Conference on Neural
Information Processing, pp. 572–581, 2017, doi: 10.1007/978-3-319-70096-0_60.
31. J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, “FEVER: A Large-Scale Dataset for Fact Extraction and Verification,” in
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT),
2018, pp. 809–819. doi: 10.18653/v1/N18-1074.
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