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Google Trends in Infodemiology and Infoveillance: Methodology Framework.

ABSTRACT: Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

SUBMITTER: Mavragani A

PROVIDER: S-EPMC6660120 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Google Trends in Infodemiology and Infoveillance: Methodology Framework.

Mavragani Amaryllis A Ochoa Gabriela G

JMIR public health and surveillance 20190529 2

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and ...[more]

PMID: 31144671

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Project description:BACKGROUND:Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. OBJECTIVE:The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. METHODS:The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. RESULTS:Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). CONCLUSIONS:The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Project description:Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity. Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected. Methods: RSVs of the query coronavirus + covid during February 1-December 4, 2020 (period 1), and February 20-May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution Xi=X(σi,x¯i) was used to represent the trend of daily variations of xij=RSVsij . When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch's t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t<1.5 . A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values. Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets ( Max |Δ| = + 625% for Italian regions, and Max |Δ|= +175% for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities' RSVs made these datasets unusable for any kind of statistical inference. Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries' data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Further research is needed to evaluate the effectiveness of this method.

Project description:BackgroundCoronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends.MethodsHFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic "HFMD" from 2009 to 2021. Japanese tweets containing "HFMD" were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.ResultsHFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic "HFMD" presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic.ConclusionsThe significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.

Project description:BackgroundAlthough "infodemiological" methods have been used in research on coronavirus disease (COVID-19), an examination of the extent of infodemic moniker (misinformation) use on the internet remains limited.ObjectiveThe aim of this paper is to investigate internet search behaviors related to COVID-19 and examine the circulation of infodemic monikers through two platforms-Google and Instagram-during the current global pandemic.MethodsWe have defined infodemic moniker as a term, query, hashtag, or phrase that generates or feeds fake news, misinterpretations, or discriminatory phenomena. Using Google Trends and Instagram hashtags, we explored internet search activities and behaviors related to the COVID-19 pandemic from February 20, 2020, to May 6, 2020. We investigated the names used to identify the virus, health and risk perception, life during the lockdown, and information related to the adoption of COVID-19 infodemic monikers. We computed the average peak volume with a 95% CI for the monikers.ResultsThe top six COVID-19-related terms searched in Google were "coronavirus," "corona," "COVID," "virus," "corona virus," and "COVID-19." Countries with a higher number of COVID-19 cases had a higher number of COVID-19 queries on Google. The monikers "coronavirus ozone," "coronavirus laboratory," "coronavirus 5G," "coronavirus conspiracy," and "coronavirus bill gates" were widely circulated on the internet. Searches on "tips and cures" for COVID-19 spiked in relation to the US president speculating about a "miracle cure" and suggesting an injection of disinfectant to treat the virus. Around two thirds (n=48,700,000, 66.1%) of Instagram users used the hashtags "COVID-19" and "coronavirus" to disperse virus-related information.ConclusionsGlobally, there is a growing interest in COVID-19, and numerous infodemic monikers continue to circulate on the internet. Based on our findings, we hope to encourage mass media regulators and health organizers to be vigilant and diminish the use and circulation of these infodemic monikers to decrease the spread of misinformation.

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Google Trends in Infodemiology and Infoveillance: Methodology Framework.

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Google Trends in Infodemiology and Infoveillance: Methodology Framework.

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