Cyber Sleuth: Harnessing NLP and Blockchain for Twitter Based Fake News Detection

The strategy for identifying fake news incorporates in blending of Natural Language Processing (NLP) techniques, Reinforcement Learning (RL) and block chain technology. Identifying false information on Twitter is essential because of the platform's broad appeal and significant impact on public conversation. For millions of people globally, Twitter is their main source of news, which makes it a great place for information to spread quickly. The procedure commences with gathering a comprehensive dataset of news articles and their corresponding metadata, followed by NLP-based pre-processing to cleanse and tokenize the text. Pertinent attributes, such as word frequency and readability, are then extracted and utilized to train an RL agent. This agent has received training to distinguish between between authentic and fabricated news through a system of rewards and penalties. After training, the RL agent uses the traits it has collected to classify fresh news as true or false. While the potential role of block chain technology is mentioned, further explanation is necessary. This inventive strategy aims to halt the sharing of misleading information and untrue in the realm of digital news.