Author :
Reshoo Devi, Ashish Kumar, Baiju Kumar YadavJourna Name:
International Journal of Science, Engineering and Technology Volume:
14 issue:3 Year:Volume-14-issue-3 Views : 132
Abstract:
Fake news detection has become an urgent priority due to the widespread misinformation on digital platforms. A machine learning-based system is proposed in this research to classify news articles as real or fake using Natural Language Processing (NLP). The study utilizes TF-IDF vectorization and supervised learning algorithms like Logistic Regression, Naive Bayes, Random Forest, and Decision Tree to identify the most effective model for journalists, fact-checkers, and social media platforms. Logistic regression was discovered to be the most accurate model with 92% accuracy, demonstrating the power of machine learning in combating digital misinformation.