JOURNAL ARTICLE

Fake News Detection using Naive Bayes Classifier and Passive Aggressive Classifier

Abstract

The rapid growth of fake news, as well as its damaging effects on every area of our lives, has increased the demand for detecting and combating fake news. As a result, distinguishing between real and fake news is critical. However, due to the massive amount of information generated every minute on the Internet, making this distinction manually is extremely difficult. This study will suggest an approach for detecting fake news and a mechanism for implementing it on social media. In this paper, the Naive Bayes Classifier and Passive Aggressive Classifier techniques will be used to detect fake news. The results will prove that the problem of identifying fake news is possible if Machine learning and Natural Language Processing algorithm are used.

Keywords:
Naive Bayes classifier Computer science Classifier (UML) Artificial intelligence The Internet Social media Machine learning Fake news Bayes' theorem Support vector machine Internet privacy World Wide Web Bayesian probability

Metrics

9
Cited By
8.63
FWCI (Field Weighted Citation Impact)
24
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Misinformation and Its Impacts
Social Sciences →  Social Sciences →  Sociology and Political Science
Spam and Phishing Detection
Physical Sciences →  Computer Science →  Information Systems
Advanced Malware Detection Techniques
Physical Sciences →  Computer Science →  Signal Processing

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