{"title":"Topic modelling as a method for framing analysis of news coverage of the Russia-Ukraine war in 2022–2023","authors":"Anna Verbytska","doi":"10.1016/j.langcom.2024.10.004","DOIUrl":null,"url":null,"abstract":"<div><div>This study critically analyses the representation of the Russia-Ukraine war in Western (the Euronews) and Eastern (the Kyiv Post) media discourses. It examines how media organisations shape narratives through strategic framing. Employing the Natural Language Processing technique – Topic Modelling – with a generative probabilistic model LDA and a transformer-based language model BERT, the study reveals generic frames elaborated by more specific extensions, shedding light on media portrayal of economy, public opinion, security & defence, external regulations, policy evaluation, and health & safety sectors. Through Named Entity Recognition with roBERTa, Sentiment Analysis with distilBERT, and Corpus Linguistics methods with LancsBox X, interpretation of these overarching frames provides a comprehensive analysis of the nuances in narratives, societal perceptions and policy decisions amidst the ongoing war.</div></div>","PeriodicalId":47575,"journal":{"name":"Language & Communication","volume":"99 ","pages":"Pages 174-193"},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Language & Communication","FirstCategoryId":"98","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0271530924000661","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
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Abstract
This study critically analyses the representation of the Russia-Ukraine war in Western (the Euronews) and Eastern (the Kyiv Post) media discourses. It examines how media organisations shape narratives through strategic framing. Employing the Natural Language Processing technique – Topic Modelling – with a generative probabilistic model LDA and a transformer-based language model BERT, the study reveals generic frames elaborated by more specific extensions, shedding light on media portrayal of economy, public opinion, security & defence, external regulations, policy evaluation, and health & safety sectors. Through Named Entity Recognition with roBERTa, Sentiment Analysis with distilBERT, and Corpus Linguistics methods with LancsBox X, interpretation of these overarching frames provides a comprehensive analysis of the nuances in narratives, societal perceptions and policy decisions amidst the ongoing war.
期刊介绍：
This journal is unique in that it provides a forum devoted to the interdisciplinary study of language and communication. The investigation of language and its communicational functions is treated as a concern shared in common by those working in applied linguistics, child development, cultural studies, discourse analysis, intellectual history, legal studies, language evolution, linguistic anthropology, linguistics, philosophy, the politics of language, pragmatics, psychology, rhetoric, semiotics, and sociolinguistics. The journal invites contributions which explore the implications of current research for establishing common theoretical frameworks within which findings from different areas of study may be accommodated and interrelated. By focusing attention on the many ways in which language is integrated with other forms of communicational activity and interactional behaviour, it is intended to encourage approaches to the study of language and communication which are not restricted by existing disciplinary boundaries.
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