International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The proliferation of digital media and social networking platforms has led to an unprecedented spread of misinformation and fake news, posing significant threats to democratic processes, public health, and social stability. Traditional methods of news verification rely heavily on manual fact-checking, which is time-consuming, resource-intensive, and unable to scale with the exponential growth of online content. An AI Based Fake News Detection system provides a revolutionary solution by leveraging advanced machine learning algorithms, natural language processing techniques, and deep learning models to automatically identify and classify news articles as authentic or fabricated. This intelligent system analyzes multiple features including textual content, linguistic patterns, source credibility, social media engagement metrics, and metadata to make accurate predictions about news authenticity. By implementing sophisticated algorithms such as Support Vector Machines, Random Forest, Long ShortTerm Memory networks, and transformer-based models like BERT, the system achieves high accuracy in distinguishing between legitimate journalism and deliberately misleading content. The AIbased approach not only provides real-time detection capabilities but also offers scalable solutions that can process millions of articles simultaneously across multiple languages and platforms. With features including sentiment analysis, source verification, fact-checking integration, and confidence scoring, this system represents a significant advancement in combating misinformation while supporting informed decision-making in the digital age. The implementation demonstrates substantial improvements in detection accuracy, processing speed, and adaptability to emerging fake news patterns.

Keywords: Fake News Detection, Machine Learning, Natural Language Processing, Artificial Intelligence, Deep Learning, Misinformation, BERT, Classification

Downloads: | DOI: 10.17148/IJARCCE.2025.14931