Fake News Detection Using AI

Abstract – Fake information has emerge as a pervasive problem in today’s virtual age, posing substantial demanding situations to statistics integrity and public discourse. This paper examines the usage of device getting to know to come across fake news. The research makes a speciality of growing and trying out system gaining knowledge of algorithms that could distinguish among credible information assets and fraudulent facts. The review begins with a comprehensive review of the existing literature on link detection, highlighting limitations and gaps in current methodologies. Data types including real and synthetic media are collected and pre processed to extract relevant features including textual content, metadata, and language samples Using various machine learning algorithms such as logistic regression, random forest, and neural networks are used and compared for better performance in classifying false information Analytical metrics such as accuracy, precision, recall, and F1 score are used to evaluate the performance of machine learning models. Significant factor analysis is performed to identify key determinants of false positives, which contribute to the interpretation of the model. The research also explores cluster learning methods and sample clustering strategies to further enhance classification accuracy and robustness. The research results show promising results in the detection of fake news, showing the ability of machine learning to deal with misinformation The findings contribute to the advancement of fake news detection technology, and inform news organisations, social media channels and law enforcement agencies gain valuable insights for addressing the fake news epidemic.