Optimization Accuracy of Fake News Detection in social media using Multimodal Learning

The spread of false information in the digital age is a big problem because it changes how people think arounds, how politics works and how people behave on social media and the internet today. Standard detection methods that can rely on feature engineering, language indicators, or metadata often have problems with scalability and generalisability. Recent developments on natural language processing, such as Bidirectional Encoder Representations from Transformers (BERT), give a more effective method by summarize deep contextual and semantic relationships within text. In this study, BERT-based model is created to identify fake news using a dataset of news articles. The model was too though number of steps like as tokenization, embedding, and fine-tuning so it could learn patterns that distinguish between real and fake news. To measure its performance, by accuracy, precision, recall, F1-score, and AUC-ROC, and then compared the results with traditional machine learning methods. The results showed that BERT worked better than older models because it could capture subtle patterns in language, which improved detection accuracy. This also suggests that deep learning models perform more reliably when trained on large datasets. In this study, BERT proved to be an effective and practical method for detecting fake news. The work also sets a strong base for future research in automated misinformation detection.