  The rise of social media has ignited an unprecedented circulation of false
information in our society. It is even more evident in times of crises, such as
the COVID-19 pandemic. Fact-checking efforts have expanded greatly and have
been touted as among the most promising solutions to fake news, especially in
times like these. Several studies have reported the development of
fact-checking organizations in Western societies, albeit little attention has
been given to the Global South. Here, to fill this gap, we introduce a novel
Markov-inspired computational method for identifying topics in tweets. In
contrast to other topic modeling approaches, our method clusters topics and
their current evolution in a predefined time window. Through these, we
collected data from Twitter accounts of two Brazilian fact-checking outlets and
presented the topics debunked by these initiatives in fortnights throughout the
pandemic. By comparing these organizations, we could identify similarities and
differences in what was shared by them. Our method resulted in an important
technique to cluster topics in a wide range of scenarios, including an
infodemic -- a period overabundance of the same information. In particular, the
data clearly revealed a complex intertwining between politics and the health
crisis during this period. We conclude by proposing a generic model which, in
our opinion, is suitable for topic modeling and an agenda for future research.
