Hate Speech against Immigrants in Korea: A Text Mining Analysis of Comments on News about Foreign Migrant Workers and Korean Chinese Residents

초록

This study aims to examine the degree and patterns of hate speech against foreign migrant workers and Korean Chinese in Korea. We gathered news articles about the two groups posted from January 2005 to December 2014 and used text mining analysis to examine the words in the news articles with the largest number of comments. Results show that the most common type of discriminatory and hateful expressions for the two groups was crime-related one and the next common type was economy-related one. However, there were significant inter-group differences. Foreign migrant workers were more frequently viewed as economic threats to native Koreans and racial and nationality issues were more frequently mentioned. By contrast, Korean Chinese were more likely to be related with crime or fear of crime and ethnicity or compatriot issues were more commonly reported. The level of discriminatory perceptions and hateful expressions was stronger against Korean Chinese.This study aims to examine the degree and patterns of hate speech against foreign migrant workers and Korean Chinese in Korea. We gathered news articles about the two groups posted from January 2005 to December 2014 and used text mining analysis to examine the words in the news articles with the largest number of comments. Results show that the most common type of discriminatory and hateful expressions for the two groups was crime-related one and the next common type was economy-related one. However, there were significant inter-group differences. Foreign migrant workers were more frequently viewed as economic threats to native Koreans and racial and nationality issues were more frequently mentioned. By contrast, Korean Chinese were more likely to be related with crime or fear of crime and ethnicity or compatriot issues were more commonly reported. The level of discriminatory perceptions and hateful expressions was stronger against Korean Chinese.

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