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

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          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.

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          Most cited references12

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          Digital disease detection--harnessing the Web for public health surveillance.

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            Using internet searches for influenza surveillance.

            The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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              The existence of publication bias and risk factors for its occurrence.

              Publication bias is the tendency on the parts of investigators, reviewers, and editors to submit or accept manuscripts for publication based on the direction or strength of the study findings. Much of what has been learned about publication bias comes from the social sciences, less from the field of medicine. In medicine, three studies have provided direct evidence for this bias. Prevention of publication bias is important both from the scientific perspective (complete dissemination of knowledge) and from the perspective of those who combine results from a number of similar studies (meta-analysis). If treatment decisions are based on the published literature, then the literature must include all available data that is of acceptable quality. Currently, obtaining information regarding all studies undertaken in a given field is difficult, even impossible. Registration of clinical trials, and perhaps other types of studies, is the direction in which the scientific community should move.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2015
                11 June 2015
                : 10
                : 6
                : e0129179
                Affiliations
                [1 ]Arizona State University, Tempe, AZ, U. S. A.
                [2 ]Purdue University, West Lafayette, IN, U. S. A.
                [3 ]Oregon State University, Corvallis, OR, U. S. A.
                Hellas, GREECE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: ST CCC. Performed the experiments: ST GB NB SB BE JJ JJG MK ML RM VMM FN KO MLR CR. Analyzed the data: ST GB NB SB BE JJ JJG MK ML RM VMM FN KO MLR CR. Contributed reagents/materials/analysis tools: ST DE SA CCC JM. Wrote the paper: ST SA GB NB SB BE JJ JJG MK ML RM VMM FN KO MLR CR.

                Article
                PONE-D-14-51584
                10.1371/journal.pone.0129179
                4465830
                26067433
                cf85c160-eae0-4718-9d93-73b88b5dbc05
                Copyright @ 2015

                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

                History
                : 17 November 2014
                : 5 May 2015
                Page count
                Figures: 3, Tables: 3, Pages: 13
                Funding
                The parameter optimization portion of the analysis was made possible with education allocation #DMS140043 of super-computing resources from National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing initiative (ST GB NB SB BE JJ JJG MK ML RM VMM FN KO MLR CR CCC). 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 (GB SB JJ JJG MLR CR), and the Offices of the President and Provost of Arizona State University. This work was funded in part by the U.S. Department of Homeland Security (DHS) VACCINE Center award #2009-ST-061-CI0001-06 (SA DE), and was also made possible by grant #1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health (CCC). 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.
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                Research Article
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                All relevant data are within the paper and its Supporting Information files.

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