Google Trends and Media Coverage: A Comparison During the COVID-19 Pandemic

被引:0
作者
Schulze, Katja [1 ]
Loeffler, Johannes Ludwig [1 ]
Voss, Martin [1 ]
机构
[1] Free Univ Berlin, Disaster Res Unit DRU, Berlin, Germany
关键词
COVID-19; disease characteristics; disease dynamic; Germany; Google Trends; labels; media coverage; online search interest; severity; symptoms; MODEL;
D O I
10.1111/1468-5973.70045
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Since the start of the COVID-19 pandemic, infodemiological studies have utilized Google Trends (GT) data to monitor and predict changes in public interest and social behavior. However, the question posed by researchers regarding the relation between online search interest and public media coverage has remained mostly unanswered. Moreover, many studies focus their research mainly on disease labels and symptoms. Thus, this article aims to contribute to crisis research, providing a long-term analysis of Google search queries and media coverage in Germany between January 2020 and December 2022, incorporating a broad range of different keywords and categories. The study identified strong correlations between GT and public media data for the categories of disease labels, dynamics, and severity, followed by moderate to strong correlations for characteristics. GT analysis of these keywords may be suitable to monitor public awareness, validate the media impact and assess the efficacy of health communication strategies. Since the results for symptoms showed no significant relation, disease symptoms may serve as valuable keywords for surveilling or forecasting the spread of infectious diseases. The study emphasizes the significance of examining the relationship between media coverage and information-seeking behavior during pandemics and other crises.
引用
收藏
页数:13
相关论文
共 49 条
[1]   Modeling COVID-19 incidence with Google Trends [J].
Amusa, Lateef Babatunde ;
Twinomurinzi, Hossana ;
Okonkwo, Chinedu Wilfred .
FRONTIERS IN RESEARCH METRICS AND ANALYTICS, 2022, 7
[2]  
[Anonymous], 2014, BABY FOOD UK, P1, DOI DOI 10.25969/MEDIAREP/13781
[3]   Google Trends: Opportunities and limitations in health and health policy research [J].
Arora, Vishal S. ;
McKee, Martin ;
Stuckler, David .
HEALTH POLICY, 2019, 123 (03) :338-341
[4]   Experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data [J].
Behnen, Philipp ;
Kessler, Rene ;
Kruse, Felix ;
Gomez, Jorge Marx ;
Schoenmakers, Jan ;
Zerr, Sergej .
ECML PKDD 2020 WORKSHOPS, 2020, 1323 :374-384
[5]  
Betsch C., 2021, Infektionen und Gesellschaft, P28
[6]  
Beyta P., 2020, SSRN Electronic Journal, P1, DOI [10.2139/ssrn.3571360, DOI 10.2139/SSRN.3571360]
[7]  
Blake S. J., 2004, An Analysis of Human Behaviour During the WTC Disaster of 11 September 2001 Based on Published Survivor Accounts
[8]   How often people google for vaccination: Qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends [J].
Bragazzi, Nicola Luigi ;
Barberis, Ilaria ;
Rosselli, Roberto ;
Gianfredi, Vincenza ;
Nucci, Daniele ;
Moretti, Massimo ;
Salvatori, Tania ;
Martucci, Gianfranco ;
Martini, Mariano .
HUMAN VACCINES & IMMUNOTHERAPEUTICS, 2017, 13 (02) :464-469
[9]   COVID-19, lockdowns and well-being: Evidence from Google Trends [J].
Brodeur, Abel ;
Clark, Andrew E. ;
Fleche, Sarah ;
Powdthavee, Nattavudh .
JOURNAL OF PUBLIC ECONOMICS, 2021, 193
[10]  
Cohen J., 1988, Statistical Power Analysis for the Behavioral Sciences