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

• URL: http://arxiv.org/abs/2106.07344v1
• Date: Wed, 26 May 2021 15:55:58 GMT
• Title: Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
• Authors: Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed
• Abstract summary: COVID-19 has affected the world economy and the daily life routine of almost everyone. Social media platforms enable users to share information with other users who can reshare this information. We propose two CNN and RNN based models and evaluate the performance of these models on a publicly available TweetsCOV19 dataset.
• Score: 6.658785818853953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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