TY - JOUR
T1 - Mass media and the contagion of fear
T2 - The case of Ebola in America
AU - Towers, Sherry
AU - Afzal, Shehzad
AU - Bernal, Gilbert
AU - Bliss, Nadya
AU - Brown, Shala
AU - Espinoza, Baltazar
AU - Jackson, Jasmine
AU - Judson-Garcia, Julia
AU - Khan, Maryam
AU - Lin, Michael
AU - Mamada, Robert
AU - Moreno, Victor M.
AU - Nazari, Fereshteh
AU - Okuneye, Kamaldeen
AU - Ross, Mary L.
AU - Rodriguez, Claudia
AU - Medlock, Jan
AU - Ebert, David
AU - Castillo-Chavez, Carlos
N1 - Funding Information:
The authors are grateful to Jonathan Dushoff for useful discussions related to this work. The authors also wish to thank the National Science Foundation Extreme Science and Engineering Discovery Environment (NSF XSEDE) high-performance computing initiative for education allocation #DMS140043, which made possible the parameter optimization portion of this analysis. This work was funded in part by the U. S. Department of Homeland Security (DHS) VACCINE Center award #2009-ST-061-CI0001-06, and was also made possible by grant #1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health. This research was also partially supported by the Western Alliance to Expand Student Opportunities (WAESO) Louis Stokes Alliance for Minority Participation (LSAMP) Bridge to the Doctorate (BD) National Science Foundation (NSF) “Multidisciplinary STEM Solutions LSAMP Bridge to the Doctorate” Grant #HRD-1401190, and the Offices of the President and Provost of Arizona State University. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2015 Towers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2015/6/11
Y1 - 2015/6/11
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0129179
DO - 10.1371/journal.pone.0129179
M3 - Article
C2 - 26067433
AN - SCOPUS:84936088628
SN - 1932-6203
VL - 10
JO - PloS one
JF - PloS one
IS - 6
M1 - e0129179
ER -