Does my Social Media Burn? – Identify Features for the Early Detection of Company-related Online Firestorms on Twitter

Q1 Social Sciences
Kevin Koch, Alexander Dippel, Matthias Schumann
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引用次数: 6

Abstract

Online firestorms pose a serious threat to companies and cause spontaneous information asymmetry between companies and social media users, which is part of the principal-agent theory. Corporate crisis management has already developed strategies to deal with firestorms, but these strategies are more effective if the company identifies a firestorm at an early stage. Therefore, we first identify literature-based characteristics of firestorms and quantify these using data-driven features in a multiple-case study approach based on Twitter data. Secondly, we identify per case the beginning of the firestorm and days with the least fluctuation in the number of posts as reference days. Finally, we compare the features between the starting points and the reference days to determine which features are significantly different. We could identify 24 features that change significantly at the beginning of a firestorm. This enables us to determine which features a company must pay particular attention to in order to detect a firestorm at an early stage. Likewise, we discuss these features in the context of the principal-agent theory with the use of social synchrony and crowd psychology to show how these features change information diffusion and contribute to information asymmetry.

我的社交媒体烧了吗?-识别Twitter上与公司相关的在线Firestorms的早期检测功能
网络风暴对企业构成严重威胁,导致企业与社交媒体用户之间自发的信息不对称,这是委托代理理论的一部分。企业危机管理已经制定了应对火灾风暴的策略,但如果公司在早期阶段识别出火灾风暴,这些策略会更有效。因此,我们首先确定基于文献的火灾风暴特征,并在基于Twitter数据的多案例研究方法中使用数据驱动特征对这些特征进行量化。其次,我们确定每个案例的火风暴开始和帖子数量波动最小的日子作为参考日。最后,我们比较起始点和参考日之间的特征,以确定哪些特征显著不同。我们可以识别出24个特征在风暴开始时发生了显著变化。这使我们能够确定公司必须特别注意哪些特征,以便在早期阶段发现风暴。同样,我们在委托代理理论的背景下讨论了这些特征,并使用社会同步和群体心理学来展示这些特征如何改变信息扩散并导致信息不对称。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Online Social Networks and Media
Online Social Networks and Media Social Sciences-Communication
CiteScore
10.60
自引率
0.00%
发文量
32
审稿时长
44 days
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