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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62907
Volume:
10 |
5 |
May 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MCA with management
Parvatibai Genba Moze College Of Engineering, Wagholi, Pune, Maharashtra 412207
MCA with management
Parvatibai Genba Moze College Of Engineering, Wagholi, Pune, Maharashtra 412207
The proliferation of fake news has become a significant challenge to both the general public and social trust and decision making due to the rapid growth of social media and online news platforms. The research paper is based on the creation of a machine learning-based fake news detector system to classify news articles as fake and real news. The labelled dataset was gathered based on publicly accessible sources and analyzed with Natural Language Processing algorithms. The text data in the raw form were purified by use of lower-case conversion, punctuations, stop-words, tokenizing, and eliminating unwanted symbols. Term Frequency Inverse Document Frequency vectorization followed the preprocessing process in order to transform the textual data into numerical features. Four monitored machine learning models, such as Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest were implemented and compared. The dataset was separated into the training and testing subsets in a 80:20 ratio. Accuracy, precision, recall, and F1-score were used as metrics of model performance. The findings indicated that Support Vector Machine had the highest performance with 95.10% accuracy, 95.20% precision, 95.01% recall and 95.10% F1-score. The results reveal that machine learning classifiers, especially Support Vector Machine with TF-IDF features, can be effectively used to spot fake news and aid automatic misinformation detection on the Internet.
KEYWORDS
A new study suggests that machine learning techniques could be used to look at text data and find fake news. The study used supervised machine learning methods like Logistic Regression, Naive Bayes, Support Verb, and Random Forest, along with preprocessing analysis and TF-IDF feature extraction, to tell the difference between real and fake news stories. It was decided that the Support Vector Machine was the best model for the fake news spotting task because it did the best in terms of accuracy, precision, recall, and F1-score results. Another major study result says that preprocessing data and feature extraction are very important for making classification more accurate. The suggested machine learning system could be used to let automatic checking systems keep an eye on online news and social media. It is a reliable and useful way to find fake information on the Internet in general.
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Publication Date
5/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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