Evidence extraction to validate medical claims in fake news detection

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Fact-checking of online health information has become necessary due to the increasing usage of internet by people searching for medical advice. There is a plethora of false information available to the public, which can put people in harm’s way. In order to aid the factchecking process, recent research has leveraged the advancements made in NLP and deep learning techniques. Majority of the existing technology relies on the existence of labelled data, which is very limited. In this work we explored an unsupervised approach to identifying evidence sentences, which is the key task in claims verification process. We show by performing experiments on a publicly available dataset that our method achieves performance comparable to that of state-of-the-art supervised techniques. We also show how our proposed method can be adapted incases where labelled data is available.

Original languageEnglish
Title of host publicationHealth information science: proceedings of the 11th International Conference on Health Information Systems
EditorsAgma Traina, Hua Wang, Yong Zhang, Siuly Siuly, Rui Zhou, Lu Chen
PublisherSpringer Nature Switzerland
ISBN (Electronic)9783031206269
Publication statusPublished - 28 Oct 2022
Event11th International Conference on Health Information Science - virtual, online
Duration: 28 Oct 202230 Oct 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Health Information Science
Abbreviated titleHIS 2022
Cityvirtual, online


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