South Korean newspaper coverage of Yemeni refugees: analysis of topics and sentiments using machine learning techniques

This paper aims to empirically investigate how South Korean newspapers define and report refugee issues. More specifically, we identify the prevalent topics and sentiments in the newspaper coverage of Yemeni refugees by using two machine learning techniques—structural topic model (STM) and Bidirectional Encoder Representations from Transformers (BERT). The analyses show that the most prevalent topic covered in the newspapers is ‘Humanitarian residence permit’—whether the government should provide it for humanitarian reasons—, followed by the topic ‘nationalism,’ which refers to criticism and concerns about losing ‘national identity’ by accepting more foreign residents. Hence, our results show that the local newspapers are more likely to report the need for humanitarian stay permits and convey factual information such as refugee crime, while the national newspapers tend to focus on contentious issues such as ‘nationalism.’ On the other hand, we find weak evidence for the difference in covered topics in Yemeni refugee news between conservative and liberal newspapers. The findings contribute to understanding how media frames refugee problems and also have policy implications.