False medical information on social media poses harm to people's health.
While the need for biomedical fact-checking has been recognized in recent
years, user-generated medical content has received comparably little attention.
At the same time, models for other text genres might not be reusable, because
the claims they have been trained with are substantially different. For
instance, claims in the SciFact dataset are short and focused: "Side effects
associated with antidepressants increases risk of stroke". In contrast, social
media holds naturally-occurring claims, often embedded in additional context:
"`If you take antidepressants like SSRIs, you could be at risk of a condition
called serotonin syndrome' Serotonin syndrome nearly killed me in 2010. Had
symptoms of stroke and seizure." This showcases the mismatch between real-world
medical claims and the input that existing fact-checking systems expect. To
make user-generated content checkable by existing models, we propose to
reformulate the social-media input in such a way that the resulting claim
mimics the claim characteristics in established datasets. To accomplish this,
our method condenses the claim with the help of relational entity information
and either compiles the claim out of an entity-relation-entity triple or
extracts the shortest phrase that contains these elements. We show that the
reformulated input improves the performance of various fact-checking models as
opposed to checking the tweet text in its entirety.

为使社交媒体上的用户生成内容可以通过现有模型进行事实核查，本文提出了一种利用关联实体信息来压缩含有主题实体、关系和客体实体三元组成的声明式陈述或提取最短包含这些元素的短语的方法，其可提高各种验证模型的性能。