  COVID-19 has been devastating the world since the end of 2019 and has
continued to play a significant role in major national and worldwide events,
and consequently, the news. In its wake, it has left no life unaffected. Having
earned the world's attention, social media platforms have served as a vehicle
for the global conversation about COVID-19. In particular, many people have
used these sites in order to express their feelings, experiences, and
observations about the pandemic. We provide a multi-faceted analysis of
critical properties exhibited by these conversations on social media regarding
the novel coronavirus pandemic. We present a framework for analysis, mining,
and tracking the critical content and characteristics of social media
conversations around the pandemic. Focusing on Twitter and Reddit, we have
gathered a large-scale dataset on COVID-19 social media conversations. Our
analyses cover tracking potential reports on virus acquisition, symptoms,
conversation topics, and language complexity measures through time and by
region across the United States. We also present a BERT-based model for
recognizing instances of hateful tweets in COVID-19 conversations, which
achieves a lower error-rate than the state-of-the-art performance. Our results
provide empirical validation for the effectiveness of our proposed framework
and further demonstrate that social media data can be efficiently leveraged to
provide public health experts with inexpensive but thorough insight over the
course of an outbreak.
