An Effective Hybrid Model for Fake News Detection in Social Media Using Deep Learning Approach

Social media is a strong Internet platform that allows people to voice their thoughts about numerous events happening in real time at multiple locations. People comment and express their thoughts on any social media post. Meanwhile, fake news or misleading information is disseminated regarding exciting occurrences that occur in real time. A large number of Internet users read and distribute such false and fake material without knowing the nature or legitimacy of the news. This has a detrimental influence on people’s perceptions of the particular event. Classical techniques are utilized to determine the type of news distributed on Twitter, Instagram, YouTube, and Facebook. However, these techniques fail to take into account characteristics such as news-generating location, consistency, timing, and novelty. It eventually leads to a scenario in which individuals form incorrect ideas and have misleading perceptions regarding any startling news. The spread of misleading thoughts and remarks has a significant impact on real-world action results. This research article addresses these difficulties by developing a system for detecting fake news using deep learning techniques and models. To determine the originality of the news, place of generation, and longevity, two deep learning models are developed artificial neural network and a mixed classifier model of convolution neural networks and long short-term memory. This aids in detecting bogus news and removing it from the server where it is stored. The tests utilizing these coupled models increase the detection of false information in social media. Furthermore, geo-map is used in this research to aids in the regulation of fake news flowing on social media regarding unique occasions occurring all over the world.