ArticleAuthors: Jaeyoung Hur; Joonseok Yang (2024)
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 resu... 
