  Debunking misinformation is an important and time-critical task as there
could be adverse consequences when misinformation is not quashed promptly.
However, the usual supervised approach to debunking via misinformation
classification requires human-annotated data and is not suited to the fast
time-frame of newly emerging events such as the COVID-19 outbreak. In this
paper, we postulate that misinformation itself has higher perplexity compared
to truthful statements, and propose to leverage the perplexity to debunk false
claims in an unsupervised manner. First, we extract reliable evidence from
scientific and news sources according to sentence similarity to the claims.
Second, we prime a language model with the extracted evidence and finally
evaluate the correctness of given claims based on the perplexity scores at
debunking time. We construct two new COVID-19-related test sets, one is
scientific, and another is political in content, and empirically verify that
our system performs favorably compared to existing systems. We are releasing
these datasets publicly to encourage more research in debunking misinformation
on COVID-19 and other topics.
