DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Seunghyun | ko |
dc.contributor.author | Park, Kunwoo | ko |
dc.contributor.author | Lee, Minwoo | ko |
dc.contributor.author | Kim, Taegyun | ko |
dc.contributor.author | Cha, Meeyoung | ko |
dc.contributor.author | Jung, Kyomin | ko |
dc.date.accessioned | 2021-03-17T05:10:20Z | - |
dc.date.available | 2021-03-17T05:10:20Z | - |
dc.date.created | 2021-03-17 | - |
dc.date.created | 2021-03-17 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.citation | IEEE ACCESS, v.9, pp.36195 - 36206 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281592 | - |
dc.description.abstract | This 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.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network | - |
dc.type | Article | - |
dc.identifier.wosid | 000626490000001 | - |
dc.identifier.scopusid | 2-s2.0-85101743669 | - |
dc.type.rims | ART | - |
dc.citation.volume | 9 | - |
dc.citation.beginningpage | 36195 | - |
dc.citation.endingpage | 36206 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3062029 | - |
dc.contributor.localauthor | Cha, Meeyoung | - |
dc.contributor.nonIdAuthor | Yoon, Seunghyun | - |
dc.contributor.nonIdAuthor | Park, Kunwoo | - |
dc.contributor.nonIdAuthor | Lee, Minwoo | - |
dc.contributor.nonIdAuthor | Kim, Taegyun | - |
dc.contributor.nonIdAuthor | Jung, Kyomin | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Graph neural networks | - |
dc.subject.keywordAuthor | Media | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Licenses | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Recurrent neural networks | - |
dc.subject.keywordAuthor | Graph neural network | - |
dc.subject.keywordAuthor | headline incongruity | - |
dc.subject.keywordAuthor | online misinformation | - |
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