%0 Journal Article %1 Towers2015 %A Towers, S. %A Afzal, S. %A Bernal, G. %A Bliss, N. %A Brown, S. %A Espinoza, B. %A Jackson, J. %A Judson-Garcia, J. %A Khan, M. %A Lin, M. %A Mamada, R. %A Moreno, V.M. %A Nazari, F. %A Okuneye, K. %A Ross, M.L. %A Rodriguez, C. %A Medlock, J. %A Ebert, D. %A Castillo-Chavez, C. %D 2015 %I Public Library of Science %J PLoS ONE %K Article; Carlo Disease Dissemination; Ebola Ebola; Fear; Fever, Hemorrhagic Humans; Information Internet; Mass Media Media; Monte Outbreaks; Social States; United analysis; coefficient; communicable correlation disease; dissemination; dynamics; epidemic; fear; fever; health; hemorrhagic human; information linear mass mathematical media, medium; method; panic; public regression social television; videorecording; %N 6 %R http://dx.doi.org/10.1371/journal.pone.0129179 %T Mass media and the contagion of fear: The case of Ebola in America %U http://dx.doi.org/10.1371/journal.pone.0129179 %V 10 %X 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. Â\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.

@article{Towers2015, 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. {\^A}{\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.}, added-at = {2017-11-10T22:48:29.000+0100}, affiliation = {Arizona State University, Tempe, AZ, United States; Purdue University, West Lafayette, IN, United States; Oregon State University, Corvallis, OR, United States}, art_number = {e0129179}, author = {Towers, S. and Afzal, S. and Bernal, G. and Bliss, N. and Brown, S. and Espinoza, B. and Jackson, J. and Judson-Garcia, J. and Khan, M. and Lin, M. and Mamada, R. and Moreno, V.M. and Nazari, F. and Okuneye, K. and Ross, M.L. and Rodriguez, C. and Medlock, J. and Ebert, D. and Castillo-Chavez, C.}, biburl = {https://www.bibsonomy.org/bibtex/22c1eba4c088c5378472797154f71db4e/ccchavez}, coden = {POLNC}, date-added = {2017-11-10 21:45:26 +0000}, date-modified = {2017-11-10 21:45:26 +0000}, document_type = {Article}, doi = {http://dx.doi.org/10.1371/journal.pone.0129179}, interhash = {81f71b3635a7f38e06c6d3427f2f0f7e}, intrahash = {2c1eba4c088c5378472797154f71db4e}, issn = {19326203}, journal = {PLoS ONE}, keywords = {Article; Carlo Disease Dissemination; Ebola Ebola; Fear; Fever, Hemorrhagic Humans; Information Internet; Mass Media Media; Monte Outbreaks; Social States; United analysis; coefficient; communicable correlation disease; dissemination; dynamics; epidemic; fear; fever; health; hemorrhagic human; information linear mass mathematical media, medium; method; panic; public regression social television; videorecording;}, language = {English}, number = 6, publisher = {Public Library of Science}, pubmed_id = {26067433}, timestamp = {2017-11-10T22:48:29.000+0100}, title = {Mass media and the contagion of fear: The case of Ebola in America}, url = {http://dx.doi.org/10.1371/journal.pone.0129179}, volume = 10, year = 2015 }