Prachi Radheshyam Kanojia
IT
April 2026
The rapid growth of digital media and online news platforms has significantly increased the spread of fake news, leading to misinformation and adverse social impact. Detecting fake news manually is challenging due to the massive volume of online content and the speed at which it spreads. This paper presents a machine learning and natural language processing–based approach for detecting fake news by analyzing textual content. The proposed system utilizes text preprocessing techniques and applies machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest to classify news articles as fake or genuine. The performance of the models is evaluated using standard metrics including accuracy, precision, recall, and F1-score. The results demonstrate that machine learning techniques can effectively identify fake news, highlighting the potential of automated detection systems in reducing the spread of misinformation on digital platforms.
80- 91
© Airo Journals 2021
