Authors : Thanuvidhya C.; Sabipriya A.; Vikram R.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/v8mm3rn3

Scribd : https://tinyurl.com/3uzftuzt

DOI : https://doi.org/10.38124/ijisrt/26mar1778

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : With increasing use of online platforms, the spread of false information is an major concern. Existing system mainly focus on detecting misinformation but do not provide users with corrected or reliable alternatives. This paper proposes an Ethical Fake Information Rewriter that not only detects misleading content but also transforms it into accurate and responsible information. The system supports multiple input formats, including text, website links and images. Natural Language Processing techniques are used to analyse textual content, while Optical Character Recognition is applied to extract text from images. After identifying misinformation, the system rewrites the content by removing bias, harmful expressions and misleading statements while preserving the original meaning. The system is implemented using a Flask-based backend with an interactive web interface, integrating multiple application programming interfaces to enhance accuracy and reliability. This approach enhances user understanding and promotes responsible use of digital information.

Keywords : Ethical AI; Natural Language Processing; Optical Character Recognition; Content Rewriting; Misinformation.

References :

1. Allein, L., Moens, M. F., and Perrotta, D., “Preventing profiling for ethical fake news detection,” Journal of Artificial Intelligence Ethics, vol. 2, no. 1, pp. 45–59, 2022.
2. Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., and Nakov, P., “Predicting factuality of reporting and bias of news media sources,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3528–3539, 2018.
3. Bhogade, M., Deore, B., Sharma, A., Sonawane, O., and Singh, M., “A research paper on fake news detection,” International Journal of Computer Applications, vol. 12, no. 7, pp. 589–599, 2025.
4. Castillo, C., Mendoza, M., and Poblete, B., “Information credibility on Twitter,” Proceedings of the 20th International World Wide Web Conference (WWW), pp. 675–684, 2011.
5. Chen, Y., Conroy, N. J., and Rubin, V. L., “Misleading online content: Recognizing clickbait as false news,” Proceedings of the ACM Workshop on Multimodal Deception Detection, pp. 15–19, 2015.
6. Montoneri, B., “Plagiarism and ethical issues: A literature review on academic misconduct,” International Journal of Educational Integrity, vol. 17, no. 2, pp. 75–88, 2021.
7. Niu, C., Guan, Y., Wu, Y., and Zhu, J., “Retrieval augmented ethical AI for fake information rewriting,” NewsBreak Research Journal, vol. 3, pp. 65–78, 2024.
8. Niu, C., Guan, Y., Wu, Y., and Zhu, J., “VeraCT scan: Retrieval augmented fake news detection with justifiable reasoning,” NewsBreak Research Journal, vol. 3, pp. 50–64, 2024.
9. Plikynas, D., Rizgelienė, I., and Korvel, G., “Ethical implications in fake news detection systems,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 150–164, 2024.
10. Plikynas, D., Rizgelienė, I., and Korvel, G., “Systematic review of fake news, propaganda, and disinformation,” IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 120–134, 2024.