Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets

Amelie Wührl, Roman Klinger


Abstract
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.
Anthology ID:
2022.argmining-1.18
Volume:
Proceedings of the 9th Workshop on Argument Mining
Month:
October
Year:
2022
Address:
Online and in Gyeongju, Republic of Korea
Venue:
ArgMining
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
187–198
Language:
URL:
https://aclanthology.org/2022.argmining-1.18
DOI:
Bibkey:
Cite (ACL):
Amelie Wührl and Roman Klinger. 2022. Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets. In Proceedings of the 9th Workshop on Argument Mining, pages 187–198, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets (Wührl & Klinger, ArgMining 2022)
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PDF:
https://aclanthology.org/2022.argmining-1.18.pdf
Data
COVID-FactPUBHEALTHSciFact