Real-time Fake News Detection System Using AI

Abstract

In the era of digital communication, the rapid spread of fake news has emerged as a critical global challenge, undermining public trust and causing significant societal harm. This research focuses on the development of a Real-time Fake News Detection System Using AI to identify and mitigate the dissemination of misinformation across online platforms. The proposed system leverages advanced machine learning models,includingNaturalLanguageProcessing(NLP)techniquesanddeeplearningalgorithms, toanalyze the content, context, and source of news articles

Introduction

Summary:

The system uses a multimodal AI approach to detect fake news by analyzing both text and visual content in real time, integrating with social media APIs for continuous monitoring. Trained on diverse datasets with advanced feature engineering and hyperparameter tuning, it achieves high accuracy while minimizing false positives. To ensure transparency, Explainable AI (XAI) techniques explain why news is classified as fake, and a user-friendly web interface allows instant verification by users and fact-checkers.

The system also addresses challenges like adversarial attacks, data imbalance, evolving misinformation tactics, and ethical concerns surrounding censorship. Key AI methods include Natural Language Processing (NLP), Machine Learning (ML), Deep Learning, and Explainable AI.

Literature highlights models that combine linguistic analysis, multimodal fusion, semi-supervised learning, and social context for improved detection performance. Core objectives include applying text cleaning and developing real-time news classification.

Methodologies such as Logistic Regression and Support Vector Machines (SVM) are employed, using AI-extracted textual features to classify news as fake or real promptly.

Overall, this system aims to provide a scalable, efficient tool to combat digital misinformation, protect public discourse, and support democratic processes by enabling immediate and reliable fake news detection.

Conclusion

Based on the results of this experiment, the Random Forest classifier proved to be the most effective model for fake news detection, achieving an accuracy of 100%. In contrast, Logistic Regression, SVM, LSTM, and CNN all had an accuracy of 50%, indicating that they were unable to differentiate between real and fake news. TheRandomForestalgorithm\'ssuperiorperformancecanbeattributedtoitsensemblelearningapproach,where multiple decision trees work together to enhance classification accuracy and reduce overfitting. However, the perfectaccuracymaysuggestpotentialissuessuchasoverfittingtothedataset,andfurthervalidationwithalarger, more diverse dataset is necessary.

References

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Copyright

Copyright © 2025 Mr. Dipen Limbachia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.