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

Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, Jackson J, Judson-Garcia J, Khan M, Lin M, Mamada R, Moreno VM, Nazari F, Okuneye K, Ross ML, Rodriguez C, Medlock J, Ebert D, Castillo-Chavez C - PLoS ONE (2015)

Bottom Line: 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.Between 65% to 76% of the variance in all samples is described by the news media contagion model.

View Article: PubMed Central - PubMed

Affiliation: Arizona State University, Tempe, AZ, U. S. A.

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.

No MeSH data available.


Related in: MedlinePlus

Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.The relative interest in Ebola-related searches during the month of October 2014 rivaled the flu-related searches at the beginning of the A/H1N1 pandemic.
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pone.0129179.g001: Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.The relative interest in Ebola-related searches during the month of October 2014 rivaled the flu-related searches at the beginning of the A/H1N1 pandemic.

Mentions: During the recent very limited U. S. Ebola outbreak, the popularity of Ebola-related Google searches in the U. S. rivaled that of flu-related searches during the 2009 A/H1N1 pandemic (see Fig 1), but we can be confident that none (or virtually none) of the Ebola-related U. S. Internet searches or tweets arose from actual victims of Ebola in the U. S. The situation thus provides an excellent means to determine how public interest, curiosity, or panic regarding a certain topic affects social media and Internet search dynamics, and allows us to examine the influence of news media on these trends. The results of this study will thus help to inform future digital epidemiological analyses of outbreak data, possibly allowing for the correction of the effects of benign interest, information-seeking behavior, or public hysteria.


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

Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, Jackson J, Judson-Garcia J, Khan M, Lin M, Mamada R, Moreno VM, Nazari F, Okuneye K, Ross ML, Rodriguez C, Medlock J, Ebert D, Castillo-Chavez C - PLoS ONE (2015)

Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.The relative interest in Ebola-related searches during the month of October 2014 rivaled the flu-related searches at the beginning of the A/H1N1 pandemic.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4465830&req=5

pone.0129179.g001: Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.The relative interest in Ebola-related searches during the month of October 2014 rivaled the flu-related searches at the beginning of the A/H1N1 pandemic.
Mentions: During the recent very limited U. S. Ebola outbreak, the popularity of Ebola-related Google searches in the U. S. rivaled that of flu-related searches during the 2009 A/H1N1 pandemic (see Fig 1), but we can be confident that none (or virtually none) of the Ebola-related U. S. Internet searches or tweets arose from actual victims of Ebola in the U. S. The situation thus provides an excellent means to determine how public interest, curiosity, or panic regarding a certain topic affects social media and Internet search dynamics, and allows us to examine the influence of news media on these trends. The results of this study will thus help to inform future digital epidemiological analyses of outbreak data, possibly allowing for the correction of the effects of benign interest, information-seeking behavior, or public hysteria.

Bottom Line: 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.Between 65% to 76% of the variance in all samples is described by the news media contagion model.

View Article: PubMed Central - PubMed

Affiliation: Arizona State University, Tempe, AZ, U. S. A.

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.

No MeSH data available.


Related in: MedlinePlus