Representing the True and False Text Information About Human Papillomavirus Vaccines

Chieh-Li Chin, Wen-Yuh Su, Jessie Chin

Research output: Contribution to journalConference articlepeer-review

Abstract

While the virality of misinformation has been recognized as one of the significant global issues in the modern societies, few studies had examined the computational approaches to represent and identify false information in health domains. The current study aimed at using both psycholinguistic and natural language processing models to represent verified true and false texts about human papillomavirus (HPV) vaccines. Compared to the conventional word-embedding models representing texts in the levels of words, sentences or documents, results showed that introducing the embedding in the levels of propositions best differentiated the semantic representations in true and false texts. The study would advance our understandings in representing health texts and have implications on detecting false health information.
Original languageEnglish (US)
Pages (from-to)317-321
JournalProceedings of the International Symposium on Human Factors and Ergonomics in Health Care
Volume9
Issue number1
DOIs
StatePublished - Sep 2020

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