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

Cited 5 time in webofscience Cited 5 time in scopus
  • Hit : 701
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorYoon, Seunghyunko
dc.contributor.authorPark, Kunwooko
dc.contributor.authorLee, Minwooko
dc.contributor.authorKim, Taegyunko
dc.contributor.authorCha, Meeyoungko
dc.contributor.authorJung, Kyominko
dc.date.accessioned2021-03-17T05:10:20Z-
dc.date.available2021-03-17T05:10:20Z-
dc.date.created2021-03-17-
dc.date.created2021-03-17-
dc.date.issued2021-03-
dc.identifier.citationIEEE ACCESS, v.9, pp.36195 - 36206-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10203/281592-
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.languageEnglish-
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.identifier.wosid000626490000001-
dc.identifier.scopusid2-s2.0-85101743669-
dc.type.rimsART-
dc.citation.volume9-
dc.citation.beginningpage36195-
dc.citation.endingpage36206-
dc.citation.publicationnameIEEE ACCESS-
dc.identifier.doi10.1109/ACCESS.2021.3062029-
dc.contributor.localauthorCha, Meeyoung-
dc.contributor.nonIdAuthorYoon, Seunghyun-
dc.contributor.nonIdAuthorPark, Kunwoo-
dc.contributor.nonIdAuthorLee, Minwoo-
dc.contributor.nonIdAuthorKim, Taegyun-
dc.contributor.nonIdAuthorJung, Kyomin-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
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 Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 5 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0