The COVID-19 pandemic affected almost every aspect of our lives. It rapidly changed
the way we behave in our daily lives, including how we seek and access information. Social
media has become pivotal for accessing information about the pandemic, though not all
information available is reliable. Therefore, this study uses a social media mining approach to
analyze the public’s sentiment during COVID-19 pandemic through social media posts (e.g.
Twitter). Social media mining is crucial for understanding information behavior of individuals in
a time when collective action is essential. Data is being collected through tweets streaming using
terms related to coronavirus (“coronavirus” and “covid19”), and limited to tweets within the
USA. Additionally, analysis on the aggregated tweets to understand emotional content of tweets
was conducted alongside visual content (memes) related to the pandemic, which were collected
for content analysis. Text mining and sentiment analysis serve as an avenue for understanding
implicit meaning in social media posts, thus furthering a more complete understanding of
messages transmitted via social media related to COVID-19. The analysis will be correlated with
other aspects, such as timeline and pertinent activities. Understanding the process for collecting
social media data during a world crisis (pandemic), creates a context where social media data can
be analyzed through different perspectives, thus leading to a more in-depth understanding of
efforts at communication about COVID-19 (education strategies, preventive behaviors, etc.), and
the public’s response to the crisis
