Abstract

The development of social media has con- tributed to a remarkable rise in the spread of fake news. Today people rely more on online news outlets. The chance of receiving fake news on an online platform is high. As we went through a pandemic and the Covid-19 was the most absorbing topic of 2020, much news on Covid-19 was published every day in traditional media and social media. Among that news, some are fake. In this work, we have collected a new dataset for detecting fake news from traditional media on Covid-19. We have gathered more than 3000 pieces of news from traditional media out of the 170 are fake ones that were collected from fact-checking sites. Then we have tested the existing four classification algorithms with our dataset using Count Vectorizer and TF-IDF. We have merged 170 fake news with four scales of true news and analyzed the outcome.