Mass media and the contagion of fear: The case of Ebola in America

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. © 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

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

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

High performance p- and n-type light-emitting field-effect transistors employing thermally activated delayed fluorescence

Light-emitting field-effect transistors (LEFETs) are an emerging type of devices that combine light-emitting properties with logical switching function. One of the factors limiting their efficiency stems from the spin statistics of electrically generated excitons. Only 25% of them, short lived singlet states, are capable of light emission, with the other 75% being long lived triplet states that are wasted as heat due to spin-forbidden processes. Traditionally, the way to overcome this limitation is to use phosphorescent materials as additional emission channel harnessing the triplet excitons. Here, an alternative strategy for triplet usage in LEFETs in the form of thermally activated delayed fluorescence (TADF) is presented. Devices employing a TADF capable material, 4CzIPN (2,4,5,6-tetra[9H-carbazol-9-yl]isophthalonitrile), in both n-type and p-type configurations are shown. They manifest excellent electrical characteristics, consistent brightness in the range of 100-1,000 cd m and external quantum efficiency (EQE) of up to 0.1%, which is comparable to the equivalent organic light-emitting diode (OLED) based on the same materials. Simulation identifies the poor light out-coupling as the main reason for lower than expected EQEs. Transmission measurements show it can be partially alleviated using a more transparent top contact, however more structural optimization is needed to tap the full potential of the device

Parameters of the Ebola-related news media contagion model of Eq 2 or Eq 3 (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.

<p>The parameter <i>f</i> is the initial fraction of the population susceptible to news media induced Ebola interest or panic (as manifested by the particular Ebola-related Internet searches or tweets in our samples). The parameter <i>β</i> is the transmission rate, and 1/<i>γ</i> is the average time, in days between an individual viewing an Ebola-related news video, and performing an Ebola-related Google search or tweet. The average number of particular Internet searches or tweets in our samples inspired by a single news video in the initial susceptible population is <i>fβ</i>. The numbers in the square brackets represent the 95% confidence intervals.</p><p>Parameters of the Ebola-related news media contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e002" target="_blank">Eq 2</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e003" target="_blank">Eq 3</a> (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.</p

The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of Eq 1, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).

<p>Also shown are the <i>R</i>² under the assumption that the temporal patterns in the data samples cause the temporal patterns in the news videos (<i>I</i> → <i>V</i>). The p-values testing for Granger causality between the various time series are also shown.</p><p>The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e001" target="_blank">Eq 1</a>, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).</p

Fits of the news media contagion model, and a simple linear regression model, to the sources of data used in this study.

<p>The fits of the linear regression model (shown in blue) tend to be generally too low in the beginning and too high at the end. In contrast, the contagion model (red line) accounts for the boredom effect, where people become more and more disinclined to perform Ebola-related searches or tweets after an extended period of exposure to Ebola-related news-coverage. Incorporation of this dynamic in the model yields significantly better fits to the data compared to the regression model.</p

Time series of Ebola-related news media, Twitter, and Google search data used in this study.

<p>The samples consist of six weeks of data ending October 31^st, 2014. The first case of Ebola confirmed in the U. S. occurred on September 29, 2014. The temporal trends in the data samples are highly inter-correlated, with a minimum of 70% correlation between samples.</p

The percentage of the variance, <i>R</i>², of the Ebola-related Twitter and Google search samples described by the contagion model of Eq 2 or Eq 3 (as appropriate to the sample); shown are the <i>R</i>² of the model fit to the full sample, the first half of the sample (model validation training sample), and the extrapolated model prediction for the remaining half of the sample (model validation test sample).

<p>Also shown are the <i>R</i>² for the statistical model, which linearly regresses the data samples on the daily number of Ebola-related news videos.</p><p>The percentage of the variance, <i>R</i>², of the Ebola-related Twitter and Google search samples described by the contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e002" target="_blank">Eq 2</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e003" target="_blank">Eq 3</a> (as appropriate to the sample); shown are the <i>R</i>² of the model fit to the full sample, the first half of the sample (model validation training sample), and the extrapolated model prediction for the remaining half of the sample (model validation test sample).</p

Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.

<p>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.</p