Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89363
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dc.contributorDepartment of Englishen_US
dc.creatorTay, Den_US
dc.date.accessioned2021-03-18T03:04:43Z-
dc.date.available2021-03-18T03:04:43Z-
dc.identifier.issn0024-3841en_US
dc.identifier.urihttp://hdl.handle.net/10397/89363-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Tay, D. (2021). Automated lexical and time series modelling for critical discourse research: A case study of Hong Kong protest editorials. Lingua, 255, 103056 is available at https://dx.doi.org/10.1016/j.lingua.2021.103056.en_US
dc.subjectDiscourse analysisen_US
dc.subjectHong Kong protestsen_US
dc.subjectLIWCen_US
dc.subjectTime series analysisen_US
dc.titleAutomated lexical and time series modelling for critical discourse research : a case study of Hong Kong protest editorialsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume255en_US
dc.identifier.doi10.1016/j.lingua.2021.103056en_US
dcterms.abstractThis paper advances a novel approach to critical synchronic and diachronic discourse analysis using automated lexical and time series modelling. It is illustrated by a case study of near-daily editorials (N = 201; 300,081 words) from 9 June to 2 October 2019 on the Hong Kong protest movement in three ideologically contrasting sources – China Daily (CD), South China Morning Post (SCMP), and Hong Kong Free Press (HKFP). Lexical analysis with Linguistic Inquiry and Word Count (LIWC) first revealed four predominant socio-psychological word categories - relativity, drive, cognitive, and affect. Overall, HKFP expresses anger at the government, CD lays blame on protestors’ violent actions, and SCMP occupies a middle position to focus on less political aspects. Time series modelling is then applied to redirect attention from these aggregated differences to how they unfold day-to-day. It was found that while positive affect words are characterized by short-term consistencies and fluctuations, most variables exhibit random variation across time. The approach allows precise description of how linguistic variables in neighbouring time periods inter-relate, offering rich interpretative possibilities for different linguistic/discourse contexts. Furthermore, determining whether a variable is ‘modelable’ offers a systematic and replicable way to interrogate the assumption that discourse inevitably serves to construe social reality.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationLingua, May 2021, v. 255, 103056en_US
dcterms.isPartOfLinguaen_US
dcterms.issued2021-05-
dc.identifier.scopus2-s2.0-85101874774-
dc.identifier.artn103056en_US
dc.description.validate202103 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0611-n01, a1601-
dc.identifier.SubFormID585, 45574-
dc.description.fundingSourceRGCen_US
dc.description.fundingText15601019en_US
dc.description.pubStatusPublisheden_US
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