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Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network

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dc.contributor.authorYoon, Seunghyun-
dc.contributor.authorPark, Kunwoo-
dc.contributor.authorLee, Minwoo-
dc.contributor.authorTAEGYUN KIM-
dc.contributor.authorMEEYOUNG CHA-
dc.contributor.authorJung, Kyomin-
dc.date.accessioned2021-11-08T04:50:02Z-
dc.date.available2021-11-08T04:50:02Z-
dc.date.created2021-04-21-
dc.date.issued2021-
dc.identifier.issn2169-3536-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/10612-
dc.description.abstractThis paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.-
dc.language영어-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleLearning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000626490000001-
dc.identifier.scopusid2-s2.0-85101743669-
dc.identifier.rimsid75360-
dc.contributor.affiliatedAuthorTAEGYUN KIM-
dc.contributor.affiliatedAuthorMEEYOUNG CHA-
dc.identifier.doi10.1109/ACCESS.2021.3062029-
dc.identifier.bibliographicCitationIEEE ACCESS, v.9, pp.36195 - 36206-
dc.relation.isPartOfIEEE ACCESS-
dc.citation.titleIEEE ACCESS-
dc.citation.volume9-
dc.citation.startPage36195-
dc.citation.endPage36206-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordAuthorGraph neural networks-
dc.subject.keywordAuthorMedia-
dc.subject.keywordAuthorTraining-
dc.subject.keywordAuthorTask analysis-
dc.subject.keywordAuthorLicenses-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorRecurrent neural networks-
dc.subject.keywordAuthorGraph neural network-
dc.subject.keywordAuthorheadline incongruity-
dc.subject.keywordAuthoronline misinformation-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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