Analysis of COVID-19 Offensive Tweets and Their Targets

Abstract

During the global COVID-19 pandemic, people utilized social media platforms, especially Twitter, to spread and express opinions about the pandemic. Such discussions also drove the rise in COVID-related offensive speech. In this work, focusing on Twitter, we present a comprehensive analysis of COVID-related offensive tweets and their targets. We collected a COVID-19 dataset with over 747 million tweets for 30 months and fine-tuned a BERT classifier to detect offensive tweets. Our offensive tweets analysis shows that the ebb and flow of COVID-related offensive tweets potentially reflect events in the physical world. We then studied the targets of these offensive tweets. There was a large number of offensive tweets with abusive words, which could negatively affect the targeted groups or individuals. We also conducted a user network analysis, and found that offensive users interact more with other offensive users and that the pandemic had a lasting impact on some offensive users. Our study offers novel insights into the persistence and evolution of COVID-related offensive tweets during the pandemic