人事管理局数据泄露:对Twitter上共享情绪反应的调查

MIS Q. Pub Date : 2022-05-24 DOI:10.25300/misq/2022/15596
Eric Bachura, Rohit Valecha, Rui Chen, H. Rao
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引用次数: 11

摘要

本文调查了Twitter用户在大规模数据泄露事件后的共同情绪反应,这是一场被称为2015年人事管理办公室(OPM)数据泄露的危机事件。由于敏感和个人身份信息的暴露,这一漏洞影响了数百万人的生活。我们采用数据探索方法,分析了在公开通知泄露事件发生后,随后讨论的18,000多条推文信息。结果分析表明,尽管焦虑、愤怒和悲伤的情绪最初可能表现得不稳定,但总的来说,这些情绪的公开表现与泄密事件的情境意识相对应。此外,我们的分析发现,这种关系延伸到情感的分享,表明那些参与谈话的人聚集在一种共享的情感体验中。最后,对随后的对话进行深入分析,确定了这些情绪最显著的对话驱动因素,揭示了与每种情绪最显著相关的违约概念。基于结果,我们提出了从这一分析中得出的命题,以告知在此类危机事件持续期间出现的情绪反应特征。本研究的结果可以在应对数据泄露等危机事件的背景下为组织实践和政策制定提供信息。
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The OPM Data Breach: An Investigation of Shared Emotional Reactions on Twitter
This paper investigates the shared emotional responses of Twitter users in the aftermath of a massive data breach, a crisis event known as the Office of Personnel Management (OPM) data breach of 2015. This breach impacted the lives of several million individuals due to the exposure of sensitive and personally identifying information. We take a data exploration approach to analyzing over 18,000 tweet messages of the ensuing discussion that took place after public notification that the breach had occurred. The resulting analysis reveals that although the emotions of anxiety, anger, and sadness may initially appear erratic, at an aggregate level, the public display of these emotions corresponds to the situational awareness of the breach event. Further, our analysis finds that this relationship extends to the sharing of emotions, indicating that those participating in the conversation congregate around a sense of shared emotional experience. Finally, an in-depth analysis of the ensuing dialogue identifies the most salient conversational drivers of these emotions, revealing breach concepts most significantly related to each emotion. Based on the results, we present propositions that draw from this analysis to inform emotional response characteristics that emerge over the duration of such crisis events. The results of this study can inform organizational practices and policy making in the context of response to crisis events such as data breaches.
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