ARCHIVES
Original Article
An AI-Driven Framework for Fake News Classification and Sentiment Prediction Using Transformer and RNN Models
Waman R. Parulekar1
Supriya S. Surve2
Gousiya A. Khanche3
Sanika C. Joshi4
Hemangi D. Naik5
1 2 3 4 Department of MCA, Finolex Academy of Management and Technology, Ratnagiri, India. 5 Department of Computer Engineering, Yashwantrao Bhonsale Institute of Technology, Sindhudurg, India.
Published Online: March-April 2026
Pages: 293-299
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602041
References
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sentiment analysis,” arXiv preprint arXiv: 2304.00636, Apr. 2023.
8. Y. Liu, J. Chen, and Z. Yang, “Emotion detection for misinformation: A review,” arXiv preprint arXiv: 2311.00671, Nov. 2023.
9. H. Xu, Y. Wu, and Y. Liu, “Ample: Emotion-aware multimodal fusion prompt learning for fake news detection,” arXiv preprint arXiv:
2410.15591, Oct. 2024.
10. S. Bhardwaj and S. Kim, “Fake social media news and distorted campaign detection using sentiment and machine learning,” Heliyon, vol.
10, no. 2, p. e27357, Feb. 2024, doi: 10.1016/j.heliyon.2024.e27357.
11. P. Gohil, R. Joshi, and H. Gandhi, “Fake news detection using hybrid transformer models,” SRELS Journal of Information Management,
vol. 61, no. 2, pp. 85–95, Apr. 2024.
12. R. Bania, P. Laskar, and S. Choudhury, “Towards development of machine learning models for fake news detection and sentiment
analysis,” in Proc. ICIMMI, 2024, pp. 87–96.
13. J. Shaeri and A. Katanforoush, “A semi-supervised fake news detection using sentiment encoding and LSTM with self-attention,” 2024.
14. S. Saikia, M. Singh, and A. K. Singh, “A hybrid model for fake news detection: Leveraging news content and user comments,” IET
Software, vol. 15, no. 6, pp. 477–489, Dec. 2021, doi: 10.1049/ise2.12021.
15. M. Ghafoor and A. Aish, “Detection of fake news using Bi-LSTM and TF-IDF vectorization,” Journal of Innovative Computer
Engineering and Technology (JICET), vol. 9, no. 1, pp. 15–23, 2024.
16. R. Singh and S. Singh, “Fake news detection using LSTM, BiLSTM and CNN,” Journal of Advances in Science and Engineering, vol. 2,
no. 1, pp. 35–41, 2022.
17. J. Nie, H. Wang, and X. Zhang, “A comparative study of machine learning and hybrid models for fake news detection with sentiment
analysis,” in Proc. ACE, 2024.
18. M. Jamen and J. Reyes, “Fake news detection in Philippine news corpus using sentiment and topic modeling,” in Proc. Samahang Pisika
ng Pilipinas Physics Conf., 2023, pp. 1–5.
19. A. Amrutha, R. Ramesh, and M. Rajan, “Hybrid machine learning models for enhanced fake news detection,” Journal of Intelligent Data
Analysis and Computer Science, vol. 5, no. 2, pp. 90–97, 2024
20. M. Atma, “Comparative analysis of RNN, LSTM, and Bi-LSTM for fake news detection,” Jurnal Teknologi Informasi Medikom, vol. 6,
no. 2, pp. 112–119, 2024.
10.1126/science.aap9559.
2. 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.
3. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi:
10.1162/neco.1997.9.8.1735.
4. A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
5. J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,”
arXiv preprint arXiv: 1810.04805, 2018.
6. V. Sanh et al., “DistilBERT: A distilled version of BERT,” in NeurIPS Workshop, 2019.
7. A. Bounaama and B. E. A. Abderrahim, “Classifying COVID-19 related tweets with BERT-based models: Fake news detection and
sentiment analysis,” arXiv preprint arXiv: 2304.00636, Apr. 2023.
8. Y. Liu, J. Chen, and Z. Yang, “Emotion detection for misinformation: A review,” arXiv preprint arXiv: 2311.00671, Nov. 2023.
9. H. Xu, Y. Wu, and Y. Liu, “Ample: Emotion-aware multimodal fusion prompt learning for fake news detection,” arXiv preprint arXiv:
2410.15591, Oct. 2024.
10. S. Bhardwaj and S. Kim, “Fake social media news and distorted campaign detection using sentiment and machine learning,” Heliyon, vol.
10, no. 2, p. e27357, Feb. 2024, doi: 10.1016/j.heliyon.2024.e27357.
11. P. Gohil, R. Joshi, and H. Gandhi, “Fake news detection using hybrid transformer models,” SRELS Journal of Information Management,
vol. 61, no. 2, pp. 85–95, Apr. 2024.
12. R. Bania, P. Laskar, and S. Choudhury, “Towards development of machine learning models for fake news detection and sentiment
analysis,” in Proc. ICIMMI, 2024, pp. 87–96.
13. J. Shaeri and A. Katanforoush, “A semi-supervised fake news detection using sentiment encoding and LSTM with self-attention,” 2024.
14. S. Saikia, M. Singh, and A. K. Singh, “A hybrid model for fake news detection: Leveraging news content and user comments,” IET
Software, vol. 15, no. 6, pp. 477–489, Dec. 2021, doi: 10.1049/ise2.12021.
15. M. Ghafoor and A. Aish, “Detection of fake news using Bi-LSTM and TF-IDF vectorization,” Journal of Innovative Computer
Engineering and Technology (JICET), vol. 9, no. 1, pp. 15–23, 2024.
16. R. Singh and S. Singh, “Fake news detection using LSTM, BiLSTM and CNN,” Journal of Advances in Science and Engineering, vol. 2,
no. 1, pp. 35–41, 2022.
17. J. Nie, H. Wang, and X. Zhang, “A comparative study of machine learning and hybrid models for fake news detection with sentiment
analysis,” in Proc. ACE, 2024.
18. M. Jamen and J. Reyes, “Fake news detection in Philippine news corpus using sentiment and topic modeling,” in Proc. Samahang Pisika
ng Pilipinas Physics Conf., 2023, pp. 1–5.
19. A. Amrutha, R. Ramesh, and M. Rajan, “Hybrid machine learning models for enhanced fake news detection,” Journal of Intelligent Data
Analysis and Computer Science, vol. 5, no. 2, pp. 90–97, 2024
20. M. Atma, “Comparative analysis of RNN, LSTM, and Bi-LSTM for fake news detection,” Jurnal Teknologi Informasi Medikom, vol. 6,
no. 2, pp. 112–119, 2024.
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