Validating a Mixed-Method Approach for Multilingual News Framing Analysis

A case study of COVID-19

Abstract

A reliable multilingual news framing analysis can shed light on the similarities and dissimilarities of journalistic practices across geographical areas and cultures, facilitating comparative studies in media discourse and framing analysis. However, there is an insufficient supply of standardizing and validating computational approaches for multilingual text analysis. To fill the gap, this study validates a multi-stage mixed-method approach, Analysis of Topic Model Networks (ANTMN), a novel approach that identifies media frames by integrating Latent Dirichlet Allocation (LDA) topic modeling and network analysis, for the news framing analysis in a multilingual context. Our multilingual ANTMN analysis standardizes the pipeline for data collection, cleaning, preprocessing, and validity assessment. By applying this approach to COVID-19 news from seven countries and regions (Germany, Hong Kong, South Korea, Taiwan, Turkey, Uganda, and the United States), this study demonstrates the reliability and validity of multilingual ANTMN analysis both quantitatively and qualitatively. We observe that ANTMN framing analysis is robust to variation in data sources and has the ability to produce generalizable results across multilingual corpora.