hosniyeh safiarian - Department of Management Information Systems, science and research branch, Islamic Azad University, Tehran, Iran
Mohammad Jafar Tarokh - Department of Industrial Engineering, Khaje Nasir Toosi University of Technology Tehran, Iran
MohammadAli Afshar Kazemi - Department of Industrial Management,Centeral Tehran Branch, , Islamic Azad University, Tehran, Iran

When, we consider the ubiquity of Facebook, twitter, LinkedIn, it is easy to understand how social media is woven into the fabric of our day-to-day activities. It is a suitable tool to find information about news , events , and different Issues. After corona virus outbreak, it is inspired users to understand pandemic news, mortality statistics and vaccination news. According to evidence, the diffusion of pandemic news on social medium has increased from ۲۰۲۰ and user face a ton of COVID۱۹ messages. The purpose of this paper is to determine the check-worthiness of news about COVID-۱۹ to identify and priorities news that need fact-checking. We proposed a method that is called CVMD. We extracted the feature of content. We use the deep learning approach for prediction it means that we model this problem with a binary classification problem. Our proposed method is evaluated by different measures on twitter dataset and the results show that CVMD method has a high accuracy in prediction rather than other methods.

Check-Worthiness, Covid۱۹, deep learning, Diffusion, social media