  In this paper, we present an iterative graph-based approach for the detection
of symptoms of COVID-19, the pathology of which seems to be evolving. More
generally, the method can be applied to finding context-specific words and
texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets
mentioning #COVID-19). Given the novelty of COVID-19, we also test if the
proposed approach generalizes to the problem of detecting Adverse Drug Reaction
(ADR). We find that the approach applied to Twitter data can detect symptom
mentions substantially before being reported by the Centers for Disease Control
(CDC).
