Home / Current Issue / Paper 1715927
Fake News AI-Based Detection System
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV9I10-1715927
Abstract
It is the time when a single false story can travel the globe in minutes, shaping opinions before anyone has a chance to verify it. Fake news is no longer just a nuisance — it actively destabilises democracies, fuels communal tensions, and erodes public trust in institutions. Manual fact-checking, as noble an effort as it is, simply cannot keep up with the flood of content produced every day on social media. This paper explores how Artificial Intelligence can step in to help. We built a Fake News Detection System using Natural Language Processing (NLP) and Machine Learning (ML) that reads a news article and decides whether it is real or fabricated. The system uses TF-IDF vectorization to convert text into meaningful numerical features, and a Passive Aggressive Classifier to make the final call. Trained on roughly 20,000 news records from Kaggle, the system reached 92% accuracy, 90% precision, and 91% recall — results that genuinely surprised us with how well a lightweight approach could perform. Beyond the numbers, this paper also digs into what existing research has missed and where future systems need to go.
Keywords
Fake News Detection, Artificial Intelligence, Natural Language Processing, Machine Learning, TF-IDF, Misinformation.
References
[1] Reuters Institute for the Study of Journalism. (2023). Digital news report 2023. University of Oxford. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2023
[2] Kaliyar, R. K., Goswami, A., & Narang, P. (2021). FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Applications, 80(8), 11765–11788. https://doi.org/10.1007/s11042-020-10183-2
[3] Kaggle. (2020). Fake News Dataset. https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
[4] World Health Organization. (2020). Managing the COVID-19 infodemic. https://www.who.int/docs/default-source/coronaviruse/risk-comms-updates/update32-infodemic-mgt.pdf
[5] Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1–40. https://doi.org/10.1145/3395046
[6] Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2018). Automatic detection of fake news. Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018), 3391–3401.
[7] Ruchansky, N., Seo, S., & Liu, Y. (2017). CSI: A hybrid deep model for fake news detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM), 797–806. https://doi.org/10.1145/3132847.3132877
[8] Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600
[9] Wang, W. Y. (2017). 'Liar, Liar Pants on Fire': A new benchmark dataset for fake news detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), 422–426. https://doi.org/10.18653/v1/P17-2067
[10] Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on Twitter. Proceedings of the 20th International Conference on World Wide Web (WWW '11), 675–684. https://doi.org/10.1145/1963405.1963500
How to cite this paper
@article{1715927,
author = {Dr. Amandeep Singh Arora, Swati Gupta, Dr. Shalu Tandon, Dr. Vikas Rao Vadi, Charanpreet Kaur; Dr. Asjad Usmani},
title = {Fake News AI-Based Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {13-17},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715927.pdf},
abstract = {It is the time when a single false story can travel the globe in minutes, shaping opinions before anyone has a chance to verify it. Fake news is no longer just a nuisance — it actively destabilises democracies, fuels communal tensions, and erodes public trust in institutions. Manual fact-checking, as noble an effort as it is, simply cannot keep up with the flood of content produced every day on social media. This paper explores how Artificial Intelligence can step in to help. We built a Fake News Detection System using Natural Language Processing (NLP) and Machine Learning (ML) that reads a news article and decides whether it is real or fabricated. The system uses TF-IDF vectorization to convert text into meaningful numerical features, and a Passive Aggressive Classifier to make the final call. Trained on roughly 20,000 news records from Kaggle, the system reached 92% accuracy, 90% precision, and 91% recall — results that genuinely surprised us with how well a lightweight approach could perform. Beyond the numbers, this paper also digs into what existing research has missed and where future systems need to go.},
keywords = {Fake News Detection, Artificial Intelligence, Natural Language Processing, Machine Learning, TF-IDF, Misinformation.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1715927}
}