Fake News Detection Using BERT Model

The proposed Fake News Detection System provides an effective and intelligent solution for identifying misleading and fabricated news content using Natural Language Processing (NLP) and the BERT deep learning model. The system successfully automates the process of news verification by analyzing the contextual and semantic features of textual data and classifying news articles as either real or fake. Through its web-based interface, users can easily submit news content and obtain prediction results in real time, making the verification process simple and accessible. The implementation of modules such as user authentication, news submission, text preprocessing, BERT-based classification, result visualization, and database management demonstrates the practical applicability of the proposed system. By leveraging the contextual understanding capabilities of BERT, the system achieves reliable prediction performance and helps reduce dependence on manual fact-checking methods, which are often time-consuming and inefficient for handling large volumes of digital content. In conclusion, the developed system contributes to combating the growing problem of misinformation by providing a scalable, accurate, and user-friendly fake news detection platform. The successful integration of artificial intelligence and deep learning techniques highlights the potential of automated fact-checking systems in promoting trustworthy information sharing and creating a more reliable digital information ecosystem.

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