Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89363
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of English | en_US |
dc.creator | Tay, D | en_US |
dc.date.accessioned | 2021-03-18T03:04:43Z | - |
dc.date.available | 2021-03-18T03:04:43Z | - |
dc.identifier.issn | 0024-3841 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/89363 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_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.rights | The 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.subject | Discourse analysis | en_US |
dc.subject | Hong Kong protests | en_US |
dc.subject | LIWC | en_US |
dc.subject | Time series analysis | en_US |
dc.title | Automated lexical and time series modelling for critical discourse research : a case study of Hong Kong protest editorials | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 255 | en_US |
dc.identifier.doi | 10.1016/j.lingua.2021.103056 | en_US |
dcterms.abstract | This 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Lingua, May 2021, v. 255, 103056 | en_US |
dcterms.isPartOf | Lingua | en_US |
dcterms.issued | 2021-05 | - |
dc.identifier.scopus | 2-s2.0-85101874774 | - |
dc.identifier.artn | 103056 | en_US |
dc.description.validate | 202103 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0611-n01, a1601 | - |
dc.identifier.SubFormID | 585, 45574 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingText | 15601019 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Tay_Automated_Lexical_Time.pdf | Pre-Published version | 1.24 MB | Adobe PDF | View/Open |
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