On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
This study addresses the societal issue of online toxicity during a crisis, providing insights for researchers and policymakers, though it is incremental as it applies existing methods to new data.
The authors tackled the problem of rising antisocial behaviors during the COVID-19 pandemic by collecting and annotating over 40 million tweets, identifying new abusive lexicons and factors influencing the spread of such content.
The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities. Such toxic behaviors take a heavy toll on society, especially during these daunting times. Despite the gravity of the issue, very few studies have studied online antisocial behaviors amid the COVID-19 pandemic. In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets. Specially, we propose an annotation framework to annotate the antisocial behavior tweets automatically. We also conduct an empirical analysis of our annotated dataset and found that new abusive lexicons are introduced amid the COVID-19 pandemic. Our study also identified the vulnerable targets of antisocial behaviors and the factors that influence the spreading of online antisocial content.