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

Published online by Cambridge University Press:  07 March 2022

Mariam Alkazemi*
Affiliation:
Robertson School of Media and Culture, Virginia Commonwealth University, Richmond, Virginia, USA
Sameneh Oladi Ghadikolai
Affiliation:
School of World Studies, Virginia Commonwealth University, Richmond, Virginia, USA
Marilynn Oetjens
Affiliation:
Undergraduate Research Assistant, Virginia Commonwealth University, Richmond, Virginia, USA
Edward L. Boone
Affiliation:
Statistics and Operations Research, Virginia Commonwealth University, Richmond, Virginia, USA
*
*Corresponding author. Email: mfalkazemi@vcu.edu
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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.

Information

Type
Special Focus: Spotlight on Pedagogical Perspectives and the Politics of Representation
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of Middle East Studies Association of North America, Inc.
Figure 0

Figure 1. Google web searches, Tweets, and WSJ mentions for Ilhan Omar.

Figure 1

Figure 2. Cross-Correlation-Function plots for WSJ and Google Trends (a), Tweets and Google Trends (b), and WSJ and Tweets (c).

Figure 2

Table 1. Classification of Tweets by Terrorist and Women for “Welcome Home” and “Go Back” as well as WSJ “Go Back” articles. Counts of each classification presented.

Figure 3

Table 2. P-values associated with tests across Tweets classified as Terrorist vs. Women, Go Back vs. Go Back and Welcome Home vs. Welcome Home with comparison of the WSJ articles to total Go Back and Terror Go Back and Welcome Home Go Back. Significant differences are in bold italics. Fisher's exact test simulated p-values based on 2,000 replications are reported.