Detection of Fake News Using Transformer Model

Social media is one of the major platforms to get news and information. However, it also provides convenience for widespread of fake news. The reason at the back of fake news is to create hype in order to get the audience's attention and build negative impact on society. The fake news detection is necessary to purify the Internet environment. Various machine learning based detection algorithms are designed to detect fake news. We use attention-based transformer model on publically available dataset for detection of fake and real news. This research aims to test and compare state-of-the-art algorithm and our proposed technique in detection of fake and real news. Our result shows that 15% of the accuracy in fake news detection is improved by transformer model as compare to Hybrid CNN.