Please use this identifier to cite or link to this item: https://repository.ifla.org/handle/123456789/3402
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dc.rights.licenseCC BY 4.0en_US
dc.contributor.authorNagy, Mihály-
dc.coverage.spatialLocation::Hungaryen_US
dc.date.accessioned2024-06-25T09:25:44Z-
dc.date.available2024-06-20-
dc.date.available2024-06-25T09:25:44Z-
dc.date.issued2024-05-30-
dc.identifier.urihttps://repository.ifla.org/handle/123456789/3402-
dc.description.abstractComputational frame analysis is a highly contested, yet widely researched area of study, promising novel approaches for developing a broader understanding of news-reporting practices. Such an approach developed by Walter and Ophir (2019) is applied to inductively discover news frames used by Hungarian online news portals during the coverage of the events in Ukraine in 2014. The approach, named Analysis of Topic Model Networks, utilises LDA topic modelling and network community detection methods for inductively identifying frame packages. The applicability of the approach to Hungarian language text is explored using the BERTopic algorithm in place of LDA. Two subcorpora are analysed from the webarchive developed by Indig et al. (2019).en_US
dc.language.isoenen_US
dc.publisherInternational Federation of Library Associations and Institutions (IFLA)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectSubject::News mediaen_US
dc.subjectSubject::Conflicten_US
dc.titleTesting an Inductive Mixed-method Computational Approach to News Frame Analysis: An analysis of Hungarian online reporting of the 2014 Russia-Ukraine conflicten_US
dc.typeEvents Materialsen_US
dc.rights.holderInternational Federation of Library Associations and Institutions (IFLA)en_US
dc.audienceAudience::News Media Sectionen_US
dc.audienceAudience::Digital Humanities – Digital Scholarship Special Interest Groupen_US
dc.audienceAudience::Information Literacy Sectionen_US
ifla.oPubId0en_US
ifla.UnitUnits::Section::News Media Sectionen_US
ifla.UnitUnits::Special Interest Group::Digital Humanities – Digital Scholarship Special Interest Groupen_US
ifla.UnitUnits::Section::Information Literacy Sectionen_US
Appears in Collections:Event Materials

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