  Social media contains unfiltered and unique information, which is potentially
of great value, but, in the case of misinformation, can also do great harm.
With regards to biomedical topics, false information can be particularly
dangerous. Methods of automatic fact-checking and fake news detection address
this problem, but have not been applied to the biomedical domain in social
media yet. We aim to fill this research gap and annotate a corpus of 1200
tweets for implicit and explicit biomedical claims (the latter also with span
annotations for the claim phrase). With this corpus, which we sample to be
related to COVID-19, measles, cystic fibrosis, and depression, we develop
baseline models which detect tweets that contain a claim automatically. Our
analyses reveal that biomedical tweets are densely populated with claims (45 %
in a corpus sampled to contain 1200 tweets focused on the domains mentioned
above). Baseline classification experiments with embedding-based classifiers
and BERT-based transfer learning demonstrate that the detection is challenging,
however, shows acceptable performance for the identification of explicit
expressions of claims. Implicit claim tweets are more challenging to detect.
