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Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
Wührl, Amelie; Klinger, Roman (2022): Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets, in: Gabriella Lapesa, Jodi Schneider, Yohan Jo, u. a. (Hrsg.), Proceedings of the 9th Workshop on Argument Mining, Gyeongju: International Conference on Computational Linguistics, S. 187–198.
Faculty/Chair:
Author:
Title of the compilation:
Proceedings of the 9th Workshop on Argument Mining
Editors:
Lapesa, Gabriella
Schneider, Jodi
Jo, Yohan
Saha, Sougata
Conference:
9th Workshop on Argument Mining, Oktober 2022 ; Gyeongju
Publisher Information:
Year of publication:
2022
Pages:
Language:
English
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.
GND Keywords: ; ; ;
Maschinelles Lernen
Twitter <Softwareplattform>
Medizin
Trainingsdaten
Keywords:
Entity-based Claim Representation
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Open Access Journal:
Yes:
Type:
Conferenceobject
Activation date:
March 7, 2024
Versioning
Question on publication
Permalink
https://fis.uni-bamberg.de/handle/uniba/93882