Hatenography On Twitter During the Covid19 Pandemic in Indonesia: Hate Speech Case Against Anies Baswedan

Abstract

The Covid-19 pandemic's handling was not merely a health problem but created a discourse battle in the social media space. The policies decided by the central and local governments often provoke public reactions in the form of criticism to hate speech. DKI Jakarta Governor Anies Baswedan has one of the governments figures whose policies have often been attacked by criticism and hate speech. This study aimed at analyzing the trend of Anies Baswedan's phrases. The phrases related to the handling of COVID19 in netizen conversations in the Twitter space and collecting patterns of hate speech about Anies Baswedan in pro-government and contra-government accounts connected with the policies of handling COVID-19. This study used a virtual ethnography method against hate speech involving written verbal messages and communication objectives from Twitter users (influencers) who commented on Anies Baswedan's policies in dealing with Covid-19. The captured data come from two groups of accounts, namely government supporter and opposition. Data exploration used https://exportcomments.com/ software for comments, while trend data and public psychology (sentiment and perception) used Intelligence Socio-analytics (ISA) from March-August 2020. Data were taken from 42,393 total replies and 1,010,007 total posts for ISA. Qualitative data processing used Nvivo 12 plus to arrange phrases and speech patterns. The result showed that Anies Baswedan associated with COVID-19 had significantly increased since march 2020 and decreased in the following months. The tweets about Anies Baswedan had built public psychology in anticipatory emotions and anger, neutral and positive sentiments. The majority public's perception of Anies Baswedan is disgusting. Pro-government accounts had made Anies Baswedan the theme of conversation in their tweets. The narrative that was built was to criticize and or belittle the governor's policies and actions. Tweets with harmful contents (insulting, condescending, disgusting, swearing, and accusations) were the dominant model, triggering replies in the same pattern, but with more crude, ironic, innocent satirical content using nouns or animal names and other, more sarcastic terms.