  With the onset of the COVID-19 pandemic, news outlets and social media have
become central tools for disseminating and consuming information. Because of
their ease of access, users seek COVID-19-related information from online
social media (i.e., online news) and news outlets (i.e., offline news). Online
and offline news are often connected, sharing common topics while each has
unique, different topics. A gap between these two news sources can lead to
misinformation propagation. For instance, according to the Guardian, most
COVID-19 misinformation comes from users on social media. Without fact-checking
social media news, misinformation can lead to health threats. In this paper, we
focus on the novel problem of bridging the gap between online and offline data
by monitoring their common and distinct topics generated over time. We employ
Twitter (online) and local news (offline) data for a time span of two years.
Using online matrix factorization, we analyze and study online and offline
COVID-19-related data differences and commonalities. We design experiments to
show how online and offline data are linked together and what trends they
follow.

Social and Information Networks;Computers and Society