Topic modelling as a method for framing analysis of news coverage of the Russia-Ukraine war in 2022–2023
Anna Verbytska
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. • War narratives are framed in media discourse emphasizing certain aspects of the event. • Topic models reveal concepts iterating in these war narratives in media outlets. • Framing analysis with Topic Modelling offers new avenues for examining points of view. • Performance of LDA and BERTopic in Topic Modelling can be employed as complemental. • Corpus Linguistics methodology is instrumental in the interpretation of media frames.