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Indulgence of the Racist Harassment? The Impact of Politicians’ Behaviors on Spreading Racist Harassment on Twitter in Japan

Civil Society
Populism
Social Media
Big Data
Taehee Kim
Carl Von Ossietzky Universität Oldenburg
Taehee Kim
Carl Von Ossietzky Universität Oldenburg

Abstract

How can racist harassment emerge and spread in a short period? In this study, we examines the effect of the behavior of a public figures with social/political power on spreading racist harassment in the online discourse. Previous literature has suggested two prominent factors fomenting online harassment: weakened social norm and majority of the opinion. Previous studies pointed out that social norms are primal factor to prevent people from expressing unfair prejudices. In addition, it is well-known that persons who conceive their opinion being minority tend to refrain to express their opinion. Given the studies, online racist harassment can spread when people conceive that social norms, which forbid harassment, are weakened. It is reasonable to assume that the gesture of a powerful public figures, e.g., leading and prominent politicians such as members of cabinet, can change the perceived social norm and social majority. Consequently, they can generate the fomenting environment by expressing a supportive opinion for the harassment. This study examines whether behaviors of Japanese leading politicians, especially the members of the ruling conservative Liberal Democratic Party and the governor of Tokyo, who are well-known as a conservative politician, have impact on spreading harassment discourse. More concretely, the study utilizes Twitter posts collected in Japan in two periods: 3 weeks before and after of the Japanese anniversary of the end of the WWII (August 2017), 6 weeks before and after of the 2018 Olympic game (January - February 2018). In Twitter, the right-wing groups trying to spread racist harassment are active in the space as well. Retweet networks and number of retweets of harassment posts are mainly analyzed. The preliminary analysis of the first period of the data supports the exception above.