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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 01 | Jan 2026

p-ISSN: 2395-0072

www.irjet.net

Fake News Detection Dr.C.P.Divate1, Ms.N.R.Bhokre2, Saksham S. Shrivastav3, Vikram R. Sargar4, Adarsh K. Yevale 5, Priyanka V. Chougule 6, Onkar M. Shinde 7 1Dean, Dept of Computer Engineering, Shri Ambabai Talim Sanstha’s Sanjay Bhokare Group of Institute

Miraj(poly), Maharashtra, India 2Lecturer, Dept of Computer Engineering, Shri Ambabai Talim Sanstha’s Sanjay Bhokare Group of Institute

Miraj(poly), Maharashtra, India 3,4,5,6,7 Student Dept. of Computer Engineering, Shri Ambabai Talim Sanstha’s Sanjay Bhokare Group of Institute Miraj(poly), Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------2. PROBLEM STATEMENT Abstract - The rapid growth of social media and online news platforms has led to an unprecedented spread of misinformation and fake news, which can influence public opinion, create social unrest, and undermine trust in digital media. Manual verification of online content is timeconsuming and impractical at a large scale. This paper presents a fake news detection system based on Natural Language Processing (NLP) and rule-based analysis techniques, without the use of machine learning models.

The rapid expansion of digital news platforms and social media has significantly transformed the way information is produced, shared, and consumed. While this digital shift has made news easily accessible and enabled real-time information dissemination, it has also resulted in the widespread circulation of fake, misleading, and deliberately manipulated news content. Such misinformation can influence public opinion, distort facts, create social unrest, and generate panic among the public, especially during critical events such as elections, natural disasters, and public health emergencies.

Key Words: fake news detection; natural language processing; rule-based system; text analysis; misinformation.

Due to the massive volume of content generated every minute on online platforms, manual verification of news authenticity has become extremely time-consuming, laborintensive, and impractical. Human fact-checkers cannot efficiently keep up with the speed and scale at which false information spreads across digital platforms. As a result, fake news often reaches a large audience before it can be identified and corrected.

1. INTRODUCTION The consumption of news has shifted significantly from traditional print and broadcast media to digital platforms, including online news portals and social networking sites. This transformation has greatly enhanced the accessibility, immediacy, and reach of information, allowing users to receive real-time updates from across the globe. However, this same ease of distribution has also facilitated the rapid spread of fake, misleading, or deliberately manipulated news. Unlike traditional media, online platforms often lack rigorous editorial oversight, enabling unverified content to circulate widely within minutes.

Many existing fake news detection systems rely primarily on keyword-based filtering or shallow text analysis techniques. These traditional approaches focus on surface-level patterns rather than understanding the contextual and semantic meaning of news articles. Consequently, they often fail to detect sophisticated fake news that uses subtle language, emotional manipulation, or partially true information, leading to inaccurate or unreliable predictions.

Numerous studies have shown that false information tends to spread faster and reach a wider audience than verified news, as it often appeals to emotions, sensationalism, or confirmation bias.

Furthermore, most existing systems simply classify news as either real or fake without providing a confidence score or explanation for the decision. This lack of transparency makes it difficult for users to assess the credibility of the output or understand how reliable the classification is. Therefore, there is a critical need for an intelligent, accurate, and userfriendly fake news detection system that leverages advanced natural language processing and machine learning techniques to evaluate news credibility effectively while also providing meaningful confidence levels to users.

This issue becomes particularly critical during sensitive events such as elections, public health emergencies, pandemics, and large-scale social movements, where misinformation can influence public opinion, create panic, undermine trust in institutions, and even lead to real-world consequences. As a result, the detection and prevention of fake news have become essential challenges in the digital age, highlighting the need for automated, reliable, and scalable solutions..

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