新冠肺炎大流行期间社交媒体推特上的数字舆论运动#IndonesiaTerserah

Fajar Rizali Rakhman, Rizky Wulan Ramadhani, A. Fatoni
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引用次数: 2

摘要

#IndonesiaTerserah数字意见运动被网民用来表达对政府和公众对新冠肺炎疫情的失望。这项研究的目的是确定新冠肺炎大流行期间社区对#IndonesiaTerserah标签形成的看法或意见。所使用的研究方法是一种混合方法,将使用Netlytic和Gephi对2000条推特数据、779个参与者和863个关系进行通信网络统计计算的定量方法与使用描述和解释社交网络及其网络结构的数字意见运动分析文本的定性方法相结合。结果显示,#IndonesiaTerserah能够在@radioelshinta和@cnindonesia(热门演员)、449 Closeness Actors、@ridwanhr(Betweenness/中间演员)、@donadam68、@reiza_patters、@toperendusara1、@bangariza、@kholil78(特征向量/重要演员)的帮助下,在交流网络中创造网民心目中的流动性。网民的失望主要表现在对抑制印尼新冠肺炎人数的意识较低的人,分析值为32%;政府制定了令人困惑的政策,无法满足日常需求,分析值为21%,而两者的分析值均为11%。此外,36%的人对标签的使用有着广泛而不同的解释。
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Digital Movement of Opinion #IndonesiaTerserah on Social Media Twitter in The Covid-19 Pandemic
The #IndonesiaTerserah Digital Movement of Opinion was used by netizens to express disappointment towards the government and the public related to the Covid-19 pandemic in Indonesia. This study's purpose is to determine the perceptions or opinions formed in the community on the hashtag #IndonesiaTerserah during the Covid-19 pandemic. The research method used was a mixed method by combining quantitative methods for statistical calculations of communication networks with a sample of 2000 tweet data, 779 actors and 863 relations using Netlytic and Gephi, with the qualitative method to analyze text using the Digital Movement of Opinion, which describes and explains social networks and their network structures. The results showed that #IndonesiaTerserah was able to create mobility in the opinion of netizens in a communication network with the help of @radioelshinta and @cnnindonesia (Popular Actors), 449 Closeness Actors, @ridwanhr (Betweenness/Intermediary Actor), @donadam68, @reiza_patters, @ toperendusara1, @bangariza, @ kholil78 (Eigenvector/Significant Actor). Disappointment of netizens has mainly shown to people who were less aware of suppressing the number of Covid-19 in Indonesia with an analysis value of 32%; to the government in making confusing policies and unable to provide for daily needs with an analysis value of 21%, and to both of them at 11%. Moreover, the use of hashtags was interpreted widely and differently by 36%.
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