Detecting weak and strong Islamophobic hate speech on social media

DC FieldValueLanguage
dc.contributor.authorVidgen, Bertie-
dc.contributor.authorYasseri, Taha-
dc.date.accessioned2022-01-12T12:54:35Z-
dc.date.available2022-01-12T12:54:35Z-
dc.date.copyright2019 Taylor & Francisen_US
dc.date.issued2020-
dc.identifier.citationJournal of Information Technology and Politicsen_US
dc.identifier.issn1933-1681-
dc.identifier.urihttp://hdl.handle.net/10197/12720-
dc.description.abstractIslamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.en_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Information Technology and Politics on 13 December 2020, available online: https://doi.org/10.1080/19331681.2019.1702607en_US
dc.subjectCommunicationen_US
dc.subjectHate speechen_US
dc.subjectIslamophobiaen_US
dc.subjectPrejudiceen_US
dc.subjectSocial mediaen_US
dc.subjectNatural language processingen_US
dc.subjectMachine learningen_US
dc.subjectBig dataen_US
dc.subjectTwitteren_US
dc.subjectScienceen_US
dc.subjectSupporten_US
dc.subjectScaleen_US
dc.titleDetecting weak and strong Islamophobic hate speech on social mediaen_US
dc.typeJournal Articleen_US
dc.internal.authorcontactothertaha.yasseri@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume17en_US
dc.identifier.issue1en_US
dc.identifier.startpage66en_US
dc.identifier.endpage78en_US
dc.identifier.doi10.1080/19331681.2019.1702607-
dc.neeo.contributorVidgen|Bertie|aut|-
dc.neeo.contributorYasseri|Taha|aut|-
dc.description.othersponsorshipEngineering and Physical Sciences Research Councilen_US
dc.date.updated2021-12-02T18:42:01Z-
dc.identifier.grantidEP/N510129/1-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:Sociology Research Collection
Geary Institute Research Collection
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