Fake News Detection on Social Media Platforms Using Deep Learning Hybrid CNN-BiLSTM Technique

Most Smartphone users prefer their phones to read news via various social platforms on the Internet. The news site publishes news and provides the source of identity verification. Humans are ineffective in distinguishing between true and false facts and make false news a threat to the logical truth, undermining the credibility of democracy, journalism, and government agencies. With the development of new technologies, finding a strategy to reduce the spread of false news or rumors that could harm society in any way is critical. Online clients are usually vulnerable to attack, and all content they run on web-based network media is generally considered reliable. Therefore, mechanized identification of fake news is essential to maintain a significant online media and informal organization. Believing in rumors and pretending to be news is harmful to society. A lot of effort is required to stop the rumors and focus on correct, verifiable news pieces, especially in developing countries. As a result, we offer a model for detecting fake news, a computational style analysis based on natural language processing that can effectively recognize text retrieved from social media Fake News using hybrid Hybrid model-based deep learning techniques.