  The spread of COVID-19 has sparked racism and hate on social media targeted
towards Asian communities. However, little is known about how racial hate
spreads during a pandemic and the role of counterspeech in mitigating this
spread. In this work, we study the evolution and spread of anti-Asian hate
speech through the lens of Twitter. We create COVID-HATE, the largest dataset
of anti-Asian hate and counterspeech spanning 14 months, containing over 206
million tweets, and a social network with over 127 million nodes. By creating a
novel hand-labeled dataset of 3,355 tweets, we train a text classifier to
identify hate and counterspeech tweets that achieves an average macro-F1 score
of 0.832. Using this dataset, we conduct longitudinal analysis of tweets and
users. Analysis of the social network reveals that hateful and counterspeech
users interact and engage extensively with one another, instead of living in
isolated polarized communities. We find that nodes were highly likely to become
hateful after being exposed to hateful content. Notably, counterspeech messages
may discourage users from turning hateful, potentially suggesting a solution to
curb hate on web and social media platforms. Data and code is at
http://claws.cc.gatech.edu/covid.
