  The sudden outbreak of COVID-19 resulted in large volumes of data shared on
different social media platforms. Analyzing and visualizing these data is
doubtlessly essential to having a deep understanding of the pandemic's impacts
on people's lives and their reactions to them. In this work, we conduct a
large-scale spatiotemporal data analytic study to understand peoples' reactions
to the COVID-19 pandemic during its early stages. In particular, we analyze a
JSON-based dataset that is collected from news/messages/boards/blogs in English
about COVID-19 over a period of 4 months, for a total of 5.2M posts. The data
are collected from December 2019 to March 2020 from several social media
platforms such as Facebook, LinkedIn, Pinterest, StumbleUpon and VK. Our study
aims mainly to understand which implications of COVID-19 have interested social
media users the most and how did they vary over time, the spatiotemporal
distribution of misinformation, and the public opinion toward public figures
during the pandemic. Our results can be used by many parties (e.g.,
governments, psychologists, etc.) to make more informative decisions, taking
into account the actual interests and opinions of the people.
