Evidence map›Paper›PMID 34591781›Full record

ArticleJMIR public health and surveillance2021

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.

Wai-Kit Ming, Fengqiu Huang, Qiuyi Chen, Beiting Liang, Aoao Jiao, Taoran Liu, Huailiang Wu, Babatunde Akinwunmi, Jia Li, Guan Liu and 3 more

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 4 countries.

Wai-Kit Ming *Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0002-8846-7515
Fengqiu Huang *Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-3170-6773
Qiuyi Chen *School of Journalism and Communication, National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou, China.ORCID 0000-0001-6616-9448
Beiting Liang *College of Economics, Jinan University, Guangzhou, China.ORCID 0000-0002-6675-8442
Aoao JiaoCollege of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.ORCID 0000-0001-9088-9162
Taoran LiuDepartment of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0002-7806-1263
Huailiang WuInternational School, Jinan University, Guangzhou, China.ORCID 0000-0002-2040-3945
Babatunde AkinwunmiCenter for Genomic Medicine, Massachusetts General Hospital, Boston, Armenia.ORCID 0000-0001-8316-1552
Jia LiInternational School, Jinan University, Guangzhou, China.ORCID 0000-0001-9272-971X
Guan LiuFaculty of Computer Science, Jinan University, Guangzhou, China.ORCID 0000-0002-1272-292X
Casper J P ZhangSchool of Public Health, The University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0003-1047-0287
Jian HuangMRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary's Campus, Imperial College London, London, United Kingdom.ORCID 0000-0002-3931-5013
Qian LiuSchool of Journalism and Communication, National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou, China.ORCID 0000-0001-9070-2863
Jinan University · CNAlbany State University · USCity University of Hong Kong · HKImperial College London · GBMassachusetts General Hospital · USNanjing University of Aeronautics and Astronautics · CNUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19-related health information during the pandemic.

objectiveThe aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage.

methodsTo achieve the study objectives, we analyzed the public's information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19-related "diseases," "treatments and medical resources," "symptoms and signs," and "public measures." The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage.

resultsAcross all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19-related "diseases," in most countries, the RSV of the term "coronavirus" increased earlier than that of "covid-19"; however, around April 2020, the search volume of the term "covid-19" surpassed that of "coronavirus." Regarding the topic "treatments and medical resources," the most and least searched terms were "mask" and "ventilator," respectively. Regarding the topic "symptoms and signs," "fever" and "cough" were the most searched terms. The RSV for the term "lockdown" was significantly higher than that for "social distancing" under the topic "public health measures." In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic "treatments and medical resources" and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic "diseases" and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage.

conclusionsOur findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.

Indexed as

COVID-19Health CommunicationHumansInformation Seeking BehaviorPandemicsSARS-CoV-2Search EngineUnited StatesCOVID-19Google Trendsnews coveragepublic concernssearch peaks

Identifiers

PMID34591781
PMCPMC8691414
OpenAlexW3200269178

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.