The Process Of Multi-Class Fake News Dataset Generation

Nowadays, news plays a significant role in everyday life. Due to the increasing usage of social media and the dissemination of news by people who have access to social media, there is a problem that the validation of the news may be questioned, and people may publish fake news for their benefit. Automatic fake news detection is a complex issue. It is necessary to have up-to-date and reliable data to build an efficient model for detection. However, there are very few such datasets available for researchers. In this paper, we proposed a new fake news dataset extracted from three famous and reliable fact-checking websites. Because of the different labels used in each site, an algorithm was developed to integrated these 37 labels into five unified labels. Some experiments were conducted to show the usability and validity of the dataset.