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Mass Media and the Contagion of Fear: The Case of Ebola in America

Author

Listed:
  • Sherry Towers
  • Shehzad Afzal
  • Gilbert Bernal
  • Nadya Bliss
  • Shala Brown
  • Baltazar Espinoza
  • Jasmine Jackson
  • Julia Judson-Garcia
  • Maryam Khan
  • Michael Lin
  • Robert Mamada
  • Victor M Moreno
  • Fereshteh Nazari
  • Kamaldeen Okuneye
  • Mary L Ross
  • Claudia Rodriguez
  • Jan Medlock
  • David Ebert
  • Carlos Castillo-Chavez

Abstract

Background: In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology: We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions: We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.

Suggested Citation

  • Sherry Towers & Shehzad Afzal & Gilbert Bernal & Nadya Bliss & Shala Brown & Baltazar Espinoza & Jasmine Jackson & Julia Judson-Garcia & Maryam Khan & Michael Lin & Robert Mamada & Victor M Moreno & F, 2015. "Mass Media and the Contagion of Fear: The Case of Ebola in America," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    • Handle: RePEc:plo:pone00:0129179
    DOI: 10.1371/journal.pone.0129179
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    References listed on IDEAS

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    1. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    2. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    4. Bettencourt, Luís M.A. & Cintrón-Arias, Ariel & Kaiser, David I. & Castillo-Chávez, Carlos, 2006. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 513-536.
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    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Ebola

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    1. Branden B. Johnson, 2017. "Explaining Americans’ responses to dread epidemics: an illustration with Ebola in late 2014," Journal of Risk Research, Taylor & Francis Journals, vol. 20(10), pages 1338-1357, October.
    2. Nepp, Alexander & Okhrin, Ostap & Egorova, Julia & Dzhuraeva, Zarnigor & Zykov, Alexander, 2022. "What threatens stock markets more - The coronavirus or the hype around it?," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 519-539.
    3. Ndanguza, Denis & Mbalawata, Isambi S. & Haario, Heikki & Tchuenche, Jean M., 2017. "Analysis of bias in an Ebola epidemic model by extended Kalman filter approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 142(C), pages 113-129.
    4. Soumahoro, Souleymane, 2020. "Ethnic politics and Ebola response in West Africa," World Development, Elsevier, vol. 135(C).
    5. Barkemeyer, Ralf & Faugère, Christophe & Gergaud, Olivier & Preuss, Lutz, 2020. "Media attention to large-scale corporate scandals: Hype and boredom in the age of social media," Journal of Business Research, Elsevier, vol. 109(C), pages 385-398.

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