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.

本文旨在填补社交媒体生物医学领域中技术自动验证和虚假新闻检测方法的研究空白，使用样本与新冠肺炎、麻疹、囊性纤维化和抑郁症相关的推文，开发出一个检测自动化声明的模型，并且在嵌入式分类器和 BERT 转移学习方面进行基础分类实验，分析揭示出笔者所构建的医学推特大多数都包含自动化声明信息。