Fake News Detection Using Artificial Intelligence

Abstract

The rapid growth of social media platforms and online news sources has significantly increased the spread of fake news, posing serious challenges to individuals, organizations, and governments. Fake news can influence public opinion, create social unrest, and undermine trust in credible information sources. Traditional methods of verifying news are often time-consuming and insufficient to handle the vast amount of digital content generated daily. Therefore, there is a growing need for automated and intelligent systems capable of detecting misleading information efficiently. This paper presents an Artificial Intelligence (AI)-based approach for fake news detection using Natural Language Processing (NLP) and Machine Learning techniques. The proposed system analyses textual content from news articles and social media posts to identify patterns and characteristics associated with fake information. The methodology involves data collection, pre-processing, feature extraction, model training, and performance evaluation. Various machine learning algorithms, including Naïve Bayes, Support Vector Machine (SVM), Random Forest, and deep learning models, are explored to improve classification accuracy. Experimental results demonstrate that AI-driven models can effectively distinguish between genuine and fake news with high accuracy, precision, and recall. The findings highlight the potential of AI technologies in combating misinformation and supporting fact-checking efforts. The proposed framework offers a scalable and efficient solution for real-time fake news detection, contributing to a more reliable digital information ecosystem.