Fake News Detection Using Natural Language Processing
DOI:
https://doi.org/10.15680/IJCTECE.2026.0902008Keywords:
Fake News Detection, Natural Language Processing (NLP), Machine Learning, Text Classification, Online News Analysis, Automated Detection SystemAbstract
The rapid growth of the internet and social media has completely changed the way people consume news. Today, information spreads within seconds across digital platforms. While this has many advantages, it has also led to a serious problem rise of fake news. Fake news is misleading or false information presented as real news, and it can strongly influence public opinion. It may create social confusion, disturb political stability, and reduce people’s trust in reliable news sources. Traditionally, identifying fake news requires manual verification by experts or fact-checkers. However, this process takes a lot of time and is not practical when dealing with the huge amount of news shared online every day. Because of this challenge, there is a need for an automated and intelligent system that can quickly detect fake news. This project proposes a Fake News Detection System using Natural Language Processing (NLP). The system automatically analyses news articles by first cleaning and pre-processing the text to remove unnecessary information. Then, it extracts important features from the content and applies machine learning algorithms to classify whether the news is real or fake. By using this approach, the system increases detection accuracy, reduces human effort, and provides faster identification of misleading content. Overall, this solution helps promote safe digital communication and ensures more reliable information sharing in society
References
[1] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake News Detection on Social Media: A Data Mining Perspective,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22–36, 2017.
Available: https://doi.org/10.1145/3137597.3137600
[2] H. Ahmed, I. Traore, and S. Saad, “Detection of Online Fake News Using N-gram Analysis and Machine Learning Techniques,” International Conference on Intelligent, Secure, and Dependable Systems, 2017.
Available: https://ieeexplore.ieee.org
[3] H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi, “Truth of Varying Shades: Analysing Language in Fake News and Political Fact-Checking,” Proceedings of EMNLP, 2017.
Available: https://aclanthology.org
[4] 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.
Available: https://doi.org/10.1002/pra2.2015.145052010082
[5] X. Zhou and R. Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Computing Surveys, vol. 53, no. 5, 2020.
Available: https://doi.org/10.1145/3395046
[6] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011
Available: https://jmlr.org/papers/v12/pedregosa11a.html
[7] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009.
Available: https://www.nltk.org/book/
[8] kaggle, “Fake and Real News Dataset.”
Available: https://www.kaggle.com
[9] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019.
Available: https://arxiv.org/abs/1810.04805
