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COVID-19 has affected the world economy and the daily life routine of almosteveryone. It has been a hot topic on social media platforms such as Twitter,Facebook, etc. These social media platforms enable users to share informationwith other users who can reshare this information, thus causing thisinformation to spread. Twitter's retweet functionality allows users to sharethe existing content with other users without altering the original content.Analysis of social media platforms can help in detecting emergencies duringpandemics that lead to taking preventive measures. One such type of analysis ispredicting the number of retweets for a given COVID-19 related tweet. Recently,CIKM organized a retweet prediction challenge for COVID-19 tweets focusing onusing numeric features only. However, our hypothesis is, tweet text may play avital role in an accurate retweet prediction. In this paper, we combine numericand text features for COVID-19 related retweet predictions. For this purpose,we propose two CNN and RNN based models and evaluate the performance of thesemodels on a publicly available TweetsCOV19 dataset using seven differentevaluation metrics. Our evaluation results show that combining tweet text withnumeric features improves the performance of retweet prediction significantly.

Understanding Information Spreading Mechanisms During COVID-19 Pandemic  by Analyzing the Impact of Tweet Text and User Features for Retweet  Prediction