Classifying Fake Statements Using Natural Language Processing

This paper presents an NLP-driven approach to identifying fake statements made by public figures. The system employs natural language processing (NLP) techniques to analyze textual data and classify statements as true or false. Given the rise of digital media, misinformation has become a global challenge, influencing public opinion and political discourse. Fake news spreads rapidly through social and mainstream media, making fact-checking increasingly difficult. The proposed system processes statements independently, without relying on metadata, using techniques such as tokenization, stemming, and stop-word removal. This method enhances the accuracy of classification by focusing on the linguistic features of statements. Future improvements may incorporate additional contextual data to refine detection accuracy further. Developed using Python and MySQL, this system provides a scalable and efficient solution to combat misinformation, contributing to more reliable information dissemination in the digital age.