TRUTHGUARD MACHINE LEARNING BASED FAKE NEWS DETECTION

Fake news detection is a difficult task that has become increasingly important with the proliferation of social media and the web. In this study, we propose to use machine learning ensembles to automatically classify news articles as real or fake. We study different text features that can distinguish fake content and train combinations of different machine learning algorithms using different ensemble methods. The performance of the proposed learner ensemble approach is evaluated on four real-world datasets and found to outperform individual learners. The study highlights the potential of using machine learning techniques to detect fake news on websites, which can help reduce the spread of misinformation and its negative impact on areas such as politics, finance and healthcare.

 

KEYWORDS - Fake news detection, machine learning, classify news articles, real or fake, text features, web applications, misinformation, politics, finance, healthcare

 

- Fake news detection machine learning classify news articles real or fake text features web applications misinformation politics finance healthcare