Suppr超能文献

恐怖袭击加剧了“我们”与“他们”的二元对立观念。

Terrorist attacks sharpen the binary perception of "Us" vs. "Them".

作者信息

Jović Milan, Šubelj Lovro, Golob Tea, Makarovič Matej, Yasseri Taha, Krstićev Danijela Boberić, Škrbić Srdjan, Levnajić Zoran

机构信息

Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia.

Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.

出版信息

Sci Rep. 2023 Aug 1;13(1):12451. doi: 10.1038/s41598-023-39035-3.

Abstract

Terrorist attacks not only harm citizens but also shift their attention, which has long-lasting impacts on public opinion and government policies. Yet measuring the changes in public attention beyond media coverage has been methodologically challenging. Here we approach this problem by starting from Wikipedia's répertoire of 5.8 million articles and a sample of 15 recent terrorist attacks. We deploy a complex exclusion procedure to identify topics and themes that consistently received a significant increase in attention due to these incidents. Examining their contents reveals a clear picture: terrorist attacks foster establishing a sharp boundary between "Us" (the target society) and "Them" (the terrorist as the enemy). In the midst of this, one seeks to construct identities of both sides. This triggers curiosity to learn more about "Them" and soul-search for a clearer understanding of "Us". This systematic analysis of public reactions to disruptive events could help mitigate their societal consequences.

摘要

恐怖袭击不仅会伤害公民,还会转移他们的注意力,这对公众舆论和政府政策有着持久的影响。然而,衡量媒体报道之外公众注意力的变化在方法上具有挑战性。在这里,我们从维基百科的580万篇文章和15起近期恐怖袭击事件的样本入手来解决这个问题。我们采用了一个复杂的排除程序,以识别因这些事件而持续获得显著关注度提升的主题。对其内容进行审视会呈现出一幅清晰的画面:恐怖袭击促使在“我们”(目标社会)和“他们”(作为敌人的恐怖分子)之间建立起鲜明的界限。在此过程中,人们试图构建双方的身份认同。这引发了对了解“他们”更多信息的好奇心,并促使人们进行深刻反思以更清楚地了解“我们”。这种对公众对破坏性事件反应的系统分析有助于减轻其社会后果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fa1/10394060/0212e611d566/41598_2023_39035_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验