A Twitter network and discourse analysis of the Rana Plaza collapse

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS Applied Network Science Pub Date : 2023-11-07 DOI:10.1007/s41109-023-00587-y
Kai Bergermann, Margitta Wolter
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引用次数: 1

Abstract

Abstract Ten years after the collapse of the Rana Plaza textile factory in Dhaka, Bangladesh that killed over 1000 factory workers, the event has become a symbol for the desolate working conditions in fast fashion producer countries in the global south. We analyze the global Twitter discourse on this event over a three week window around the collapse date over the years 2013–2022 by a mixture of network-theoretic quantitative and discourse-theoretic qualitative methods. In particular, key communicators and the community structure of the discourse participants are identified using a multilayer network modeling approach and the interpretative patterns of the key communicator’s tweets of all years are analyzed using the sociology of knowledge approach to discourse. This combination of quantitative and qualitative methods reveals that the discourse is separated into three phases: reporting, reprocessing, and commemoration. These phases can be identified by the temporal evolution, network-structural properties, and the contentual analysis of the discourse. After the negotiation of the interpretative framework in the reprocessing phase, subsequent years are characterized by its commemorative repetition as well as resulting demands by different international actor groups despite highly fluctuating participants.
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拉纳广场倒塌的推特网络与话语分析
孟加拉国首都达卡的拉纳广场(Rana Plaza)纺织厂发生坍塌事故,造成1000多名工人死亡。事故发生十年后,这一事件已成为全球南方快时尚生产国荒凉工作环境的象征。我们通过网络理论定量和话语理论定性的混合方法,在2013-2022年崩溃日期前后的三周时间内分析了全球Twitter关于这一事件的话语。特别是,使用多层网络建模方法识别关键传播者和话语参与者的社区结构,并使用话语的知识社会学方法分析历年关键传播者推文的解释模式。这种定量和定性相结合的方法揭示了话语分为三个阶段:报道、再加工和纪念。这些阶段可以通过语篇的时间演变、网络结构特征和内容分析来识别。在后处理阶段的解释框架谈判之后,随后几年的特点是其纪念性的重复以及不同国际行动者群体的要求,尽管参与者高度波动。
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来源期刊
Applied Network Science
Applied Network Science Multidisciplinary-Multidisciplinary
CiteScore
4.60
自引率
4.50%
发文量
74
审稿时长
5 weeks
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