  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.
