How are ‘immigrant workers’ represented in Korean news reporting?—A text mining approach to critical discourse analysis

The present study explores the usefulness of a text mining approach to investigating the representation of minorities in news reporting. The question is popular for scholars working in the realm of critical discourse analysis (CDA). Their typical approach is qualitative, which involves dissecting a small number of texts at the microlinguistic level. This approach has over the years come under severe criticisms for the lack of objective and reliable empirical evidence it produces for the sweeping claims it makes about the relationship between language and social structures. In response to such criticisms, the discipline has recently been embracing corpus linguistics as a way of making up for its methodological weaknesses. Yet, the corpus techniques applied to CDA have so far been largely confined to collocation and concordance analysis. Despite their proven value, these techniques are not optimal for investigating the overall topic structure and content of media texts because they are primarily designed to probe lexical relationships. Digital humanities have over the years developed a range of effective tools for analyzing the content of a large population of texts. The present study applies two such tools, co-occurrence analysis and topic modeling coupled with network analysis, to analyzing a corpus of Korean straight news reports. The analyses were effective in uncovering five major topics or themes underlying the news corpus, thereby proving the usefulness of the techniques used for the type of CDA questions explored in this article.