Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 1
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Fake News Detection Using Machine Learning Techniques

Author(s) Ms. Sonali Dinesh Chawre
Country India
Abstract The rapid growth of digital media and social networking platforms has significantly increased the spread of misinformation and fake news. Fake news can manipulate public opinion, create social unrest, and negatively impact decision-making processes. Manual fact-checking mechanisms are insufficient to handle the massive volume of online content generated every day. Therefore, automated fake news detection systems have become a critical research area.

This paper presents a machine learning-based approach for detecting fake news using Natural Language Processing (NLP) techniques. The proposed system preprocesses textual news data and applies TF-IDF feature extraction to convert text into numerical form. Multiple machine learning classifiers, including Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine (SVM), were trained and evaluated on a benchmark dataset. Experimental results show that the Random Forest classifier achieved the highest accuracy of 91.3%, demonstrating the effectiveness of classical machine learning models in fake news detection. The proposed approach provides a reliable and computationally efficient solution to combat misinformation in digital media.
Keywords Fake News Detection, Machine Learning, Natural Language Processing, TF-IDF, Classification
Field Engineering
Published In Volume 7, Issue 1, January-February 2026
Published On 2026-01-29
DOI https://doi.org/10.63363/aijfr.2026.v07i01.3125
Short DOI https://doi.org/hbmz2k
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E-ISSN 3048-7641
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