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Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes

Published online by Cambridge University Press:  07 June 2021

ALA’ ALRABABA’H*
Affiliation:
Stanford University
WILLIAM MARBLE*
Affiliation:
Stanford University
SALMA MOUSA*
Affiliation:
Yale University
ALEXANDRA A. SIEGEL*
Affiliation:
University of Colorado Boulder
*
Ala’ Alrababa’h, Ph.D. Candidate, Department of Political Science, Stanford University, and Immigration Policy Lab, Stanford University and ETH Zurich, alaa@stanford.edu.
William Marble, Ph.D. Candidate, Department of Political Science, Stanford University, wpmarble@stanford.edu.
Salma Mousa, Assistant Professor, Department of Political Science, Yale University, sm3285@yale.edu.
Alexandra A. Siegel, Assistant Professor, Department of Political Science, University of Colorado Boulder, and Immigration Policy Lab, Stanford University and ETH Zurich, alexandra.siegel@colorado.edu.
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Abstract

Can exposure to celebrities from stigmatized groups reduce prejudice? To address this question, we study the case of Mohamed Salah, a visibly Muslim, elite soccer player. Using data on hate crime reports throughout England and 15 million tweets from British soccer fans, we find that after Salah joined Liverpool F.C., hate crimes in the Liverpool area dropped by 16% compared with a synthetic control, and Liverpool F.C. fans halved their rates of posting anti-Muslim tweets relative to fans of other top-flight clubs. An original survey experiment suggests that the salience of Salah’s Muslim identity enabled positive feelings toward Salah to generalize to Muslims more broadly. Our findings provide support for the parasocial contact hypothesis—indicating that positive exposure to out-group celebrities can spark real-world behavioral changes in prejudice.

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Type
Research Article
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 (http://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
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Figure 1. Attitudes toward Islam in the British Public between 2015 and 2018Note: Source: The YouGov-Cambridge Center. The vertical axis is the percentage of survey respondents stating that “there is a fundamental clash between Islam and the values of British society.” Points are weighted averages within survey waves; the trend line is a GAM fit to all 34,409 survey respondents using survey weights.

Figure 1

Figure 2. Synthetic Control Results for Hate Crimes AnalysisNote: The top panel shows the observed (solid line) and imputed (dashed line) monthly hate crime rates in Merseyside. The bottom panel shows the difference between the observed and imputed outcomes. In the posttreatment period, this is the estimate of the treatment effect. The black line shows the estimates obtained for Merseyside, and the gray lines show the estimates obtained when we treat each of the control units as if it were treated. The fact that the Merseyside estimates are consistently lower than the control group estimates provides evidence that our treatment effect estimates are unlikely to be due to chance.

Figure 2

Figure 3. Synthetic Control Results for All Crime Types in MerseysideNote: The black line shows the treatment effect estimate for hate crimes and the gray lines show treatment effect estimates for each of 14 types of crimes defined by the U.K. Home Office. To generate estimates on comparable scales across crime types, the treatment effect estimates are expressed as a percentage of the pretreatment mean for each crime type. The estimated treatment effect on hate crimes is consistently more negative than the estimate for any other crime outcome.

Figure 3

Figure 4. Synthetic Control Results for Twitter DataNote: The top panel shows the observed (solid line) and imputed (dashed line) monthly proportion of anti-Muslim tweets in Liverpool F.C. fans’ tweets that are relevant to Muslims or Islam. The bottom panel shows the difference between the observed and imputed outcomes. In the posttreatment period, this is the estimate of the ATT for Liverpool compared with that of other prominent English clubs.

Figure 4

Figure 5. Coefficient Plots Representing the Main Effect of the Religiosity Treatment on the Four Outcomes Relative to the Pure Control ConditionNote: The top outcome represents the first principal component of the other three outcomes and has a mean of zero and unit variance. The other three outcomes are binary. The bars show 95% robust confidence intervals.

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