Tweeting about abusive comments and misogyny in South Korea following the suicide of Sulli, a female K-pop star: Social and semantic network analyses
DOI:
https://doi.org/10.3145/epi.2021.sep.05Keywords:
Celebrity bashing, Misogyny, Twitter, Social media, Social network analysis, Semantic network analysis, South KoreaAbstract
This study examined the development of the public discussion on Twitter about the abusive comments specific to misogynistic discourse after the suicide of Sulli, a female celebrity in South Korea. Both the pattern of social networking between the users and the semantic representations of user responses were analyzed from a social network perspective using a large-scale Twitter dataset. A total of 37,101 tweets generated by 25,258 users were collected and analyzed. The findings of the network analysis suggest that hubs and authorities on Twitter were closely connected to each other and contributed to promoting the public discussion about abusive comments in response to her death. The results of the semantic network analysis suggested that her death, presumably due in part to continuous hateful comments from trolls, evoked an open discussion about the deeply rooted abusive comments and misogyny that are prevalent in South Korea. Users perceived that sensational news coverage about celebrities and unethical journalistic practices led to abusive comments and her death. The users shared their observations that gendered hate speech contributed to Sulli´s bullying. Dominant words that referred to Sulli´s sexual harassment show the ways in which haters had bullied her, as well as the criticism of online harassment. The results imply that the issue of online misogyny was closely associated with abusive comments in the public consciousness. This study verified the role of celebrities in increasing awareness about social issues and word-of-mouth dissemination even after a death. This study also offers methodological insights by demonstrating how social network analysis can be used to analyze public discussion using big data.
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