Real Time Verification of Fake News using Machine Learning Algorithms and Natural Language

The rise of fake news on digital platforms has become a major societal issue, shaping public opinion and eroding trust in legitimate news sources. As misinformation spreads rapidly, it becomes increasingly difficult to discern fact from fiction, leading to harmful consequences in various domains such as politics, public health, and social relationships. To combat this, there is a growing need for advanced systems that can accurately detect and classify fake news. This project aims to develop a robust machine learning system, leveraging Natural Language Processing (NLP) techniques and machine learning algorithms, to identify deceptive information in textual data with high precision. By focusing on automation, this system promises to scale effectively and provide a timely solution to the growing problem of fake news. The proposed solution incorporates a pre-processing phase that includes Part-of-Speech (POS) tagging, a technique used to analyze the grammatical structure of the text. POS tagging helps in understanding the syntactic roles of words, which can significantly enhance the ability of the model to detect subtle linguistic patterns often associated with misinformation. The system will align these POS-tagged texts with their original content, ensuring that the underlying meaning is preserved while enabling further analysis. This pre-processing is a crucial step before integrating advanced models, such as General Pre-trained Transformers (GPT), which are known for their strong language modeling capabilities. The combination of these techniques will ensure the model achieves a high level of accuracy in detecting fake news.