NEURAL NETWORKS FOR DETECTING FAKE NEWS AND MISINFORMATION: AN AI-POWERED FRAMEWORK FOR SECURING DIGITAL MEDIA AND SOCIAL PLATFORMS

The growing concern of fake news and information in contemporary society threatens the integrity of democracy and global security. Social media and on-line news websites are now considered to be some of the primary channels of fake news dissemination since they are supported by engagement-based content promotion algorithms and bot accounts from adversaries. Organic fact-checking cannot cope with the current flood of fake news and thus there is a need for machine learning (ML)-based solutions. As much as this research focus on general neural networks, this work mainly concentrates on deep learning models in dealing with fake news and misinformation detection; CNNs, RNNs, LSTM, BERT, GPT-3, and RoBERTa. The performance of these models is assessed by employing the benchmark datasets including Fake Newsnet, LIAR, PHEME, and PolitiFact, and the evaluation is made based on accuracy and computational time along with studying model’s compatibility with various types of fake news. Empirical evidence shows that the Transformer-based models improve on the traditional machine learning resulting in more than 95 % precision with enhanced contextual meaning. However, computational cost is still a drawback, and in order to overcome this, better and more efficient hybrid models are needed. Likewise, the current study also addresses some of the critical linguistic and metadata elements such as sentiments, source reliability, and social interactions that define this phenomenon. In terms of error analysis, this research finds that political misinformation represents the most significant area of difficulty for AI models while underlining the importance of domain-specific training and non-stopping model updates. The proposed AI-based framework uses NLP and social network analysis to improve the process of real-time misinformation detection, which can solve the problem of security in digital media and platforms. This study advances knowledge on fake news detection using artificial intelligence and paves way for new approaches on the further development of artificial intelligence fact-checking, ethical issues concerning artificial intelligence, and integration of explainable artificial intelligence in the fight against fake news.