{"title":"主题建模是对 2022-2023 年俄乌战争新闻报道进行框架分析的一种方法","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":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":null,\"pages\":null},\"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}","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}
引用次数: 0
摘要
本研究批判性地分析了西方(《欧洲新闻报》)和东方(《基辅邮报》)媒体对俄乌战争的表述。研究探讨了媒体组织如何通过战略框架塑造叙事。该研究采用自然语言处理技术--主题建模(Topic Modelling)--以及生成概率模型 LDA 和基于转换器的语言模型 BERT,揭示了由更具体的扩展部分所阐述的通用框架,揭示了媒体对经济、舆论、安全与国防、外部法规、政策评估以及健康与安全领域的描述。通过使用 roBERTa 进行命名实体识别,使用 distilBERT 进行情感分析,以及使用 LancsBox X 进行语料库语言学分析,对这些总体框架的解释提供了对正在进行的战争中的叙述、社会观念和政策决定的细微差别的全面分析。
Topic modelling as a method for framing analysis of news coverage of the Russia-Ukraine war in 2022–2023
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.