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dc.contributor.authorMishra, Pushkaren
dc.contributor.authorDel Tredici, Marcoen
dc.contributor.authorGiannakoudaki, Helen Yannakoudakisen
dc.contributor.authorShutova, Ekaterinaen
dc.date.accessioned2019-09-23T23:30:17Z
dc.date.available2019-09-23T23:30:17Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/297031
dc.description.abstractAbuse on the Internet represents a significant societal problem of our time. Previous re-search on automated abusive language detec-tion in Twitter has shown that community-based profiling of users is a promising tech-nique for this task.However, existing ap-proaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heteroge-neous graph-structured modeling of communi-ties significantly advances the current state of the art in abusive language detection.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAbusive Language Detection with Graph Convolutional Networksen
dc.typeConference Object
prism.endingPage2150
prism.startingPage2145
dc.identifier.doi10.17863/CAM.44089
dcterms.dateAccepted2019-05-07en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-05-07en
dc.contributor.orcidGiannakoudaki, Helen Yannakoudakis [0000-0002-4429-7729]
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.funder-project-idCambridge Assessment (unknown)
pubs.conference-nameIn Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics.en
pubs.conference-start-date2019-06-02en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International