CoChat
Sign Up
Cited by 291Misinformation and Its ImpactsData-Driven Disease SurveillanceCommunication and COVID-19 Impact

COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study.

Open access
Rovetta, Alessandro, Bhagavathula, Akshaya Srikanth·2020·JMIR Public Health and Surveillance
DOI 10.2196/19374PMC PMC7202310

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication. Objective: We investigated online search behavior related to the COVID-19 outbreak and the attitudes of “infodemic monikers” (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy. Methods: By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results. Results: Keywords such as “novel coronavirus,” “China coronavirus,” “COVID-19,” “2019-nCOV,” and “SARS-COV-2” were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were “face masks,” “amuchina” (disinfectant), “symptoms of the novel coronavirus,” “health bulletin,” and “vaccines for coronavirus.” The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome. Conclusions: We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/19374

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 35

  1. [2]Mollema L Harmsen IA Broekhuizen E Clijnk R De Melker H Paulussen T Kok G Ruiter R Das E Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in The Netherlands in 2013 J Med Internet Res 2015 5 26 17 5 e128 10.2196/jmir.3863 26013683 26013683 PMC4468573 · DOI
  2. [3]Chen Y Zhang Y Xu Z Wang X Lu J Hu W Avian Influenza A (H7N9) and related Internet search query data in China Sci Rep 2019 7 18 9 1 10434 10.1038/s41598-019-46898-y 10.1038/s41598-019-46898-y 31320681 31320681 PMC6639335 · DOI
  3. [4]Mohamed NA Knowledge, Attitude and Practice on Bats-Borne Diseases among Village Residents: A Pilot Study MH 2018 12 31 13 2 48 57 10.17576/MH.2018.1302.05 · DOI
  4. [5]Zeraatkar K Ahmadi M Trends of infodemiology studies: a scoping review Health Info Libr J 2018 6 04 35 2 91 120 10.1111/hir.12216 29729073 29729073 · DOI
  5. [6]Tang L Bie B Park S Zhi D Social media and outbreaks of emerging infectious diseases: A systematic review of literature Am J Infect Control 2018 9 46 9 962 972 10.1016/j.ajic.2018.02.010 29628293 29628293 PMC7115293 · DOI · Paper page
  6. [7]Eysenbach G SARS and population health technology J Med Internet Res 2003 6 30 5 2 e14 10.2196/jmir.5.2.e14 12857670 12857670 PMC1550560 · DOI
  7. [8]Mavragani A Ochoa G Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis J Big Data 2018 9 6 5 1 10.1186/s40537-018-0140-9 · DOI
  8. [9]Roccetti M Marfia G Salomoni P Prandi C Zagari RM Gningaye Kengni FL Bazzoli F Montagnani M Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts JMIR Public Health Surveill 2017 8 09 3 3 e51 10.2196/publichealth.7004 28793981 28793981 PMC5569247 · DOI
  9. [10]Bragazzi NL Alicino C Trucchi C Paganino C Barberis I Martini M Sticchi L Trinka E Brigo F Ansaldi F Icardi G Orsi A Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis PLoS One 2017 9 21 12 9 e0185263 10.1371/journal.pone.0185263 28934352 28934352 PMC5608413 · DOI · Paper page
  10. [12]Eysenbach G Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet J Med Internet Res 2009 3 27 11 1 e11 10.2196/jmir.1157 19329408 19329408 PMC2762766 · DOI · Paper page
  11. [13]Mavragani A Ochoa G Google Trends in Infodemiology and Infoveillance: Methodology Framework JMIR Public Health Surveill 2019 5 29 5 2 e13439 10.2196/13439 31144671 31144671 PMC6660120 · DOI · Paper page
  12. [14]Fung IC Fu K Ying Y Schaible B Hao Y Chan C Tse ZT Chinese social media reaction to the MERS-CoV and avian influenza A (H7N9) outbreaks Infect Dis Poverty 2013 12 20 2 1 31 10.1186/2049-9957-2-31 24359669 24359669 PMC3878123 · DOI
  13. [15]Hossain L Kam D Kong F Wigand RT Bossomaier T Social media in Ebola outbreak Epidemiol Infect 2016 3 04 144 10 2136 2143 10.1017/s095026881600039x 26939535 PMC9150578 · DOI
  14. [16]Carneiro H Mylonakis E Google Trends: a web-based tool for real-time surveillance of disease outbreaks Clin Infect Dis 2009 11 15 49 10 1557 64 10.1086/630200 19845471 19845471 · DOI
  15. [17]Li C Chen L Chen X Zhang M Pang C Chen H Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020 Euro Surveill 2020 3 25 10 2000199 10.2807/1560-7917.ES.2020.25.10.2000199 32183935 PMC7078825 32183935 · DOI · Paper page
  16. [18]Pulido CM Villarejo-Carballido B Redondo-Sama G Gómez A COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information International Sociology 2020 4 15 026858092091475 10.1177/0268580920914755 · DOI
  17. [19]Pulido Rodríguez C Villarejo Carballido B Redondo-Sama G Guo M Ramis M Flecha R False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information? RIMCIS 2020 4 13 1 10.17583/rimcis.2020.5386 · DOI
  18. [21]Liew S Khoo E Cheah W Goh P Ibrahim H We have to write and share valid and reliable information on COVID-19 Malays Fam Physician 2020 15 1 1 32284797 32284797 PMC7136682
  19. [22]Mavragani A Ochoa G Google Trends in Infodemiology and Infoveillance: Methodology Framework JMIR Public Health Surveill 2019 5 29 5 2 e13439 10.2196/13439 31144671 31144671 PMC6660120 · DOI · Paper page
  20. [23]Strzelecki A Rizun M Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China Int. J. Onl. Eng 2020 4 08 16 04 139 10.3991/ijoe.v16i04.13531 · DOI
  21. [24]Hu Z Yang Z Li Q Zhang A Huang Y Infodemiological study on COVID-19 epidemic and COVID-19 infodemic Preprints.org 2020 3 16 10.20944/preprints202002.0380.v3 · DOI
  22. [25]Husnayain A Fuad A Su EC Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan Int J Infect Dis 2020 3 12 10.1016/j.ijid.2020.03.021 32173572 PMC7270523 32173572 · DOI · Paper page
  23. [26]Chen E Lerman K Ferrara E COVID-19: The First Public Coronavirus Twitter Dataset arxiv.org 2020 3 16
  24. [27]Hernández-García Ignacio Giménez-Júlvez Teresa Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study JMIR Public Health Surveill 2020 4 01 6 2 e18717 10.2196/18717 32217507 32217507 PMC7117090 · DOI
  25. [32]Househ M Communicating Ebola through social media and electronic news media outlets: A cross-sectional study Health Informatics J 2016 9 26 22 3 470 8 10.1177/1460458214568037 25656678 25656678 · DOI
  26. [33]Kwak H An J Salminen J Jung S Jansen B What we read, what we search: Media attention and public attention among 193 countries 2018 4 18 Proceedings of the 2018 World Wide Web Conference 2018 April 23 Geneva 893 902 10.1145/3178876.3186137 · DOI
  27. [34]Southwell BG Dolina S Jimenez-Magdaleno K Squiers LB Kelly BJ Zika Virus-Related News Coverage and Online Behavior, United States, Guatemala, and Brazil Emerg Infect Dis 2016 7 22 7 1320 1 10.3201/eid2207.160415 10.3201/eid2207.160415 27100826 27100826 PMC4918164 · DOI
  28. [35]Chandler C Fairhead J Kelly A Leach M Martineau F Mokuwa E Parker M Richards P Wilkinson A Ebola: limitations of correcting misinformation The Lancet 2015 4 385 9975 1275 1277 10.1016/s0140-6736(14)62382-5 25534188 · DOI
  29. [36]Ortiz-Martínez Yeimer Jiménez-Arcia Luisa F Yellow fever outbreaks and Twitter: Rumors and misinformation Am J Infect Control 2017 7 01 45 7 816 817 10.1016/j.ajic.2017.02.027 28343702 28343702 · DOI
  30. [37]Gesser-Edelsburg A Diamant A Hijazi R Mesch GS Correcting misinformation by health organizations during measles outbreaks: A controlled experiment PLoS One 2018 12 19 13 12 e0209505 10.1371/journal.pone.0209505 30566485 30566485 PMC6300261 · DOI
  31. [38]Vijaykumar S Nowak G Himelboim I Jin Y Managing social media rumors and misinformation during outbreaks Am J Infect Control 2018 7 46 7 850 10.1016/j.ajic.2018.03.014 29661633 29661633 · DOI
  32. [39]Shimizu K 2019-nCoV, fake news, and racism The Lancet 2020 2 395 10225 685 686 10.1016/s0140-6736(20)30357-3 PMC7133552 32059801 · DOI
  33. [40]Mejova Y Kalimeri K Advertisers jump on coronavirus bandwagon: Politics, news, and business arxiv.org 2020 3 02
  34. [43]Chung RY Li MM Anti-Chinese sentiment during the 2019-nCoV outbreak The Lancet 2020 2 395 10225 686 687 10.1016/s0140-6736(20)30358-5 PMC7133618 32122469 · DOI · Paper page
  35. [46]Rodríguez-Morales Alfonso J MacGregor K Kanagarajah S Patel D Schlagenhauf P Going global - Travel and the 2019 novel coronavirus Travel Med Infect Dis 2020 1 33 101578 10.1016/j.tmaid.2020.101578 32044389 32044389 PMC7128681 · DOI

Cited by

Sorted by citations

Authors
Rovetta, A., & Bhagavathula, A. S.
Affiliations
United Arab Emirates University (AE)
Corresponding author
Bhagavathula, Akshaya Srikanth
ORCID
Venue
JMIR Public Health and Surveillance
Publisher
JMIR Publications Inc.
Published
2020-05-05
Volume
6
Issue
2
Pages
e19374
ISSN
2369-2960 (ISSN-L)
DOI
10.2196/19374
PMC
PMC7202310
OpenAlex
W3019512118
Cited by
291
References
1
Citations by year
2026: 4 · 2025: 11 · 2024: 15 · 2023: 39 · 2022: 60 · 2021: 109 · 2020: 53
FWCI
81.47
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Public Health and Surveillance (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
COVID-19 pandemic, infodemic, misinformation, fake news, online search behavior, Google Trends, racism, face masks, regional disparities, Italy
MeSH terms
Betacoronavirus, COVID-19, SARS-CoV-2, Attitude to Health, Communication, Disease Outbreaks, Humans, Italy, Deception, Mass Media, Pneumonia, Viral, Truth Disclosure, Information Storage and Retrieval, Coronavirus Infections, Internet, Information Seeking Behavior, Health Communication, Pandemics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review