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