Towers Sherry, Afzal Shehzad, Bernal Gilbert, Bliss Nadya, Brown Shala, Espinoza Baltazar, Jackson Jasmine, Judson-Garcia Julia, Khan Maryam, Lin Michael, Mamada Robert, Moreno Victor M, Nazari Fereshteh, Okuneye Kamaldeen, Ross Mary L, Rodriguez Claudia, Medlock Jan, Ebert David, Castillo-Chavez Carlos
Arizona State University, Tempe, AZ, U. S. A.
Purdue University, West Lafayette, IN, U. S. A.
PLoS One. 2015 Jun 11;10(6):e0129179. doi: 10.1371/journal.pone.0129179. eCollection 2015.
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.
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.
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.
2014年9月29日美国出现首例输入性埃博拉病例后的数周内,媒体对这起规模极小的疫情进行了大量报道,报道程度与对国家公共卫生的实际威胁极不相称;截至2014年10月底,全美仅有4例实验室确诊的埃博拉病例。公众对这些事件高度关注,首例确诊病例后的一个月内,与埃博拉相关的互联网搜索和推文达数百万条,反映了这一点。过去有人提议利用互联网搜索和推文趋势进行疫情的实时预测(这一领域被称为“数字流行病学”),但如何消除公众恐慌造成的偏差一直是个难题。就美国有限的埃博拉疫情而言,我们知道疫情期间源自美国的与埃博拉相关的搜索和推文仅仅是出于公众兴趣或恐慌,这为确定这些动态如何影响此类数据以及新闻媒体如何推动这些趋势提供了前所未有的途径。
我们研究了截至2014年10月31日的六周内美国每日与埃博拉相关的互联网搜索和推特数据。电视新闻报道数据来自两个主要新闻网络上每日出现的与埃博拉相关的新闻视频数量。我们将数学传播模型的参数应用于这些数据,以确定新闻报道是否是与埃博拉相关的互联网和推特数据时间模式的一个重要因素。
我们发现了显著的传播证据,每一则与埃博拉相关的新闻视频都引发了数万条与埃博拉相关的推文和互联网搜索。新闻媒体传播模型描述了所有样本中65%至76%的方差。