Attribute Agenda Setting on Twitter and the Wall Street Journal: The Case of Congresswoman Ilhan Omar

IF 0.2 Q3 AREA STUDIES
Mariam F. Alkazemi, Sameneh Oladi Ghadikolai, Marilynn Oetjens, E. Boone
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引用次数: 0

Abstract

Abstract This study examines attributes associated with U.S. Congresswoman Ilhan Omar. Omar's unique intersectionality of identities as a Black, immigrant, Muslim woman presents a rich case study for an examination of attribute agenda setting. A sample of Tweets, all Wall Street Journal articles referencing the congresswoman, and Google searches were obtained for the hashtags (“IlhanOmar,” “GoBack,” and “WelcomeHome.”) from May 2019 to January 2020. The Tweets and articles were then coded for a variety of dimensions. Analysis of the frequencies of words associated with the coding and the hashtags showed that the majority of the messages were negative despite the hashtag for which they were collected. A cross-correlation time series analysis showed that articles and editorials published by the Wall Street Journal predicted spikes in Google searches and Twitter messages. This article pinpoints underlying sociological phenomena about the representations of gender, race, religion, and immigration status to show the arguments that link attributes of Ilhan Omar together on Twitter.
推特和华尔街日报的属性议程设置:国会女议员伊尔汗·奥马尔的案例
摘要本研究考察了与美国国会女议员伊尔汗·奥马尔相关的特质。奥马尔作为一名黑人、移民和穆斯林女性,其独特的身份交叉性为审查属性议程设置提供了丰富的案例研究。从2019年5月到2020年1月,我们获得了推特、《华尔街日报》所有引用这位女议员的文章以及谷歌搜索标签(“IlhanOmar”、“GoBack”和“WelcomeHome”)的样本。然后,推特和文章被编码为各种维度。对与编码和标签相关的单词频率的分析表明,尽管收集的是标签,但大多数消息都是负面的。一项相互关联的时间序列分析显示,《华尔街日报》发表的文章和社论预测了谷歌搜索和推特消息的激增。这篇文章指出了性别、种族、宗教和移民身份表征的潜在社会学现象,以展示推特上将伊尔汗·奥马尔的属性联系在一起的论点。
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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CiteScore
0.30
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