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

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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.

Related collections

Most cited references 20

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Google trends: a web-based tool for real-time surveillance of disease outbreaks.

Herman Anthony Carneiro, Eleftherios Mylonakis (2009)

Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.

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Social media and outbreaks of emerging infectious diseases: A systematic review of literature

Lu Tang, Bijie Bie, Sung-Eun Park … (2018)

Highlights • A systematic review of published articles on social media and emerging infectious disease. • Three major approaches are identified: assessment of public opinion, organization's social media use, and evaluation of information accuracy. • More theorization and methodologic rigor is needed.

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Is Open Access

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

Liesbeth Mollema, Irene Anhai Harmsen, Emma Broekhuizen … (2015)

Background In May 2013, a measles outbreak began in the Netherlands among Orthodox Protestants who often refuse vaccination for religious reasons. Objective Our aim was to compare the number of messages expressed on Twitter and other social media during the measles outbreak with the number of online news articles and the number of reported measles cases to answer the question if and when social media reflect public opinion patterns versus disease patterns. Methods We analyzed measles-related tweets, other social media messages, and online newspaper articles over a 7-month period (April 15 to November 11, 2013) with regard to topic and sentiment. Thematic analysis was used to structure and analyze the topics. Results There was a stronger correlation between the weekly number of social media messages and the weekly number of online news articles (P<.001 for both tweets and other social media messages) than between the weekly number of social media messages and the weekly number of reported measles cases (P=.003 and P=.048 for tweets and other social media messages, respectively), especially after the summer break. All data sources showed 3 large peaks, possibly triggered by announcements about the measles outbreak by the Dutch National Institute for Public Health and the Environment and statements made by well-known politicians. Most messages informed the public about the measles outbreak (ie, about the number of measles cases) (93/165, 56.4%) followed by messages about preventive measures taken to control the measles spread (47/132, 35.6%). The leading opinion expressed was frustration regarding people who do not vaccinate because of religious reasons (42/88, 48%). Conclusions The monitoring of online (social) media might be useful for improving communication policies aiming to preserve vaccination acceptability among the general public. Data extracted from online (social) media provide insight into the opinions that are at a certain moment salient among the public, which enables public health institutes to respond immediately and appropriately to those public concerns. More research is required to develop an automatic coding system that captures content and user’s characteristics that are most relevant to the diseases within the National Immunization Program and related public health events and can inform official responses.

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Author and article information

Contributors

Akshaya Srikanth Bhagavathula:

ORCID: https://orcid.org/0000-0002-0581-7808

Institute of Public Health, College of Medicine and Health SciencesUnited Arab Emirates UniversityAl AinAbu Dhabi, 17666United Arab Emirates971 54322 ext 6187akshaypharmd@gmail.com

Journal

Journal ID (nlm-ta): JMIR Public Health Surveill

Journal ID (iso-abbrev): JMIR Public Health Surveill

Journal ID (publisher-id): JPH

Title: JMIR Public Health and Surveillance

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2369-2960

Publication date Collection: Apr-Jun 2020

Publication date (Electronic): 5 May 2020

Volume: 6

Issue: 2

Electronic Location Identifier: e19374

Affiliations

[1 ] Mensana srls Research and Disclosure Division Brescia Italy

[2 ] Institute of Public Health, College of Medicine and Health Sciences United Arab Emirates University Abu Dhabi United Arab Emirates

Author notes

Corresponding Author: Akshaya Srikanth Bhagavathula akshaypharmd@ 123456gmail.com

Author information

Alessandro Rovetta https://orcid.org/0000-0002-4634-279X

Akshaya Srikanth Bhagavathula https://orcid.org/0000-0002-0581-7808

Article

Publisher ID: v6i2e19374

DOI: 10.2196/19374

PMC ID: 7202310

PubMed ID: 32338613

SO-VID: fe280b53-1243-4196-85f8-99e817d58fdf

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

History

Date received : 15 April 2020

Date revision requested : 17 April 2020

Date revision received : 22 April 2020

Date accepted : 22 April 2020

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