google trends case study

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• Digital Marketing

Power of Google Trends in Market Research: 8 Essential Uses

Google is the most used search engine, with 99,000 searches every second. Imagine using this data of billions of daily searches to draw insights for your needs. Google Trends is a powerful tool provided by Google that allows users to explore and analyse the popularity of search queries over time.

Google Trends offers valuable insights into users’ interests, preferences, and behaviour worldwide with its intuitive interface and comprehensive data. By providing data on search volume, regional interest, related queries, and trending topics, Google Trends helps individuals and businesses understand the dynamics of search behaviour and make informed decisions.

Whether you’re a marketer, entrepreneur, researcher, or curious individual, Google Trends empowers you to harness the power of search data and stay ahead in a rapidly evolving digital landscape. By leveraging these search insights, you can make data-driven decisions, enhance your online presence, and seize opportunities in today’s dynamic marketplace.

Uses of Google Trends

Google Trends can help you analyse search trends and patterns. You can gain a better understanding of consumer interests and make data-driven decisions. Here’s how you can use Google Trends for market research:

1. Explore Trending Topics

Start by exploring the “ Trending Searches ” section on the Google Trends homepage. This section displays the latest search trends and hot topics. By reviewing these trends, you can identify emerging interests and topics gaining popularity in real-time.

2. Compare Search Terms

Enter relevant keywords or search terms related to your industry in the search bar. Google Trends allows you to compare multiple search terms to understand their relative popularity over time and across different regions. This comparison can help you identify which topics or products are more popular or in demand.

3. Analyse Regional Interest

Use the “Interest by Region” feature to identify geographical areas where interest in your industry or product is the highest. This can help you tailor your marketing efforts and target specific regions or countries with a higher potential for success.

4. Refine Your Research

Google Trends provides filters and options to refine your research further. You can specify the time range (e.g., past 12 months) to focus on recent trends. Additionally, you can filter by categories, countries, and even specific search platforms (e.g., YouTube) to gather more specific insights.

5. Explore Related Queries

Look at the “Related queries” section to identify other search terms frequently associated with your main keyword. This can help you discover related topics, customer preferences, and potential opportunities to expand your product or service offerings.

6. Seasonal Trends And Cyclical Patterns

Google Trends can show you seasonal trends and cyclical patterns in search interest for specific keywords. Understanding these patterns can help you optimise your marketing campaigns and product launches accordingly, aligning them with peak interest periods.

7. Validate Market Demand

Use Google Trends data to validate the market demand for your product or service. By comparing the search volume of different products or features, you can assess which ones are more sought-after by consumers.

8. Inform Content and SEO strategy

Google Trends can provide insights into the type of content that resonates with your target audience. Identify trending topics or specific questions related to your industry, and create relevant content that addresses those interests. This can help improve your SEO and attract more organic traffic.

Google Trends For Market Research

Google Trends is a powerful tool that can be used to gain insights into your market. You can use Google Trends to identify emerging trends, track the competition, and track industry trends.

• You can use Google Trends to identify new product opportunities. For example, if you see that there is a sudden spike in searches for “vegan snacks,” you might consider developing a new line of vegan snacks.
• You can use Google Trends to track the competition. For example, if your competitor’s website is getting more traffic for a certain keyword, you might want to adjust your content strategy to target that keyword.
• You can use Google Trends to track industry trends. For example, if you see that there is a growing interest in “sustainable fashion,” you might want to develop a new line of sustainable clothing.

Google Trends empowers marketers to understand consumer behaviour, uncover new opportunities, and make informed decisions that can enhance their online presence and position them for success in the ever-changing digital marketplace. By harnessing the power of search data, businesses and individuals can stay ahead of the curve and thrive in a competitive landscape. Google Trends offers valuable insights into consumer behaviour and interests.

Hi, I am Tarun, and I have been working as a Digital Marketing Executive at Waffle Bytes since 2021. I am passionate about PPC and Search Engine Optimization (SEO), and I consider myself a philosopher of these field.

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• Published: 24 April 2024

Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study

• Dawei Wang 1 ,
• John Cameron Lang 2 &
• Yao-Hsuan Chen 3  

Scientific Reports volume  14 , Article number:  9470 ( 2024 ) Cite this article

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• Epidemiology
• Outcomes research

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman’s rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 ( p -value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 ( p -value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman’s rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

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Introduction.

Measles virus is one of the most infectious viruses on the planet 1 and a leading cause of death and disability-adjusted life-years lost 2 . With a basic reproduction number (i.e., the number of cases directly generated from one case in a a susceptible population) of 12–18 1 , its transmissibility far exceeds other diseases, including SARS-CoV-2, which has a reproduction number of 2.5–3.5 3 and its Omicron variant, which has a reproduction number of 8.2 4 . About 75–90% of susceptible household contacts develop the disease 5 , 6 , 7 . Before the introduction of measles vaccines, 95–98% of children were infected by the measles virus by age 18 8 , 9 , 10 , 11 .

Sixty years after effective vaccines were licensed in 1963, measles continues to cause death and diseases in children worldwide. In 2018, the World Health Organization (WHO) reported more than 140,000 measles deaths globally, mostly among children under the age of 5 12 . Complications from measles can occur in almost every organ 13 . Measles infection can also diminish previously acquired immune memory, potentially leaving individuals at risk for reinfection by previously acquired pathogens 14 . Studies during the 1970s and 1980s revealed that measles case-fatality rates ranged from 3 to 34% 15 , 16 , 17 in low- and middle-income countries (LMICs), 10–20 times higher than high-income countries 13 . Although measles vaccines are highly effective with an efficacy of 97% 18 , outbreaks still occur in places with low vaccination coverage rates. Significant, yet inconsistent, progress has been made in measles vaccination since 2000. From 2000 to 2016, measles cases worldwide decreased from 145 to 18 cases per million, after which they increased again to 120 cases per million in 2019 19 .

Although measles cases did decrease during the COVID-19 pandemic (to 22 cases per million in 2020) 19 , millions more children were susceptible to measles at the end of 2020 than in 2019. Specifically, 22.3 million children among 194 WHO member states and at least 93 million persons in 23 countries did not receive measles-containing vaccines (MCVs) because of COVID-19-related postponement of measles supplementary immunization activities (SIAs) for 2020 19 . Measles surveillance also deteriorated during COVID-19 19 . In 2020, the number of measles specimens submitted was the lowest in over a decade. Many countries did not report, and few countries (32%) achieved the measles surveillance sensitivity indicator (i.e., the proportion of cases that have an imported source) 20 .

Increased population susceptibility and suboptimal measles surveillance portend an immediate elevated risk for measles transmission and outbreaks, threatening the already fragile progress toward regional elimination goals 19 . Furthermore, measles cases were not only in low-vaccination LMICs but also in high-vaccination high-income countries. In 2018, 47 of 53 Member States of the WHO European Region reported over 84,000 confirmed measles cases. Cases rose by 300% during the first 3 months of 2019 compared with the same period in 2018 21 . Although endemic measles was declared “eliminated” from the United States 22 , more than 1200 confirmed cases were reported in 31 states in 2019 23 .

The deteriorated surveillance over an increased susceptible population of one of the most infectious viruses highlights the value of real-time surveillance systems for measles. The WHO has recommended the Moving Epidemic Method (MEM) as a tool for assessing the severity of epidemics 24 , 25 . We previously applied the MEM to Google Trends search data for respiratory syncytial virus (RSV) to demonstrate the feasibility of using Google Trends as a data source for real-time monitoring of RSV outbreaks 26 . This approach complements existing surveillance systems to monitor disease outbreaks in real-time, especially in countries with limited or no sentinel network surveillance. An important step in validating this surveillance approach is to obtain both Google Trends search data and clinical case data to verify that these data are highly correlated and result in equivalent estimates for outbreak thresholds. In this study, we aim to explore the feasibility of extending this surveillance approach to other diseases, using measles as a worked example. Compared to previous work for RSV, which has no widespread immunization program, 81% and 71% of children had received 1 and 2 doses of measles-containing vaccines respectively in 183 WHO member states by the end of 2021 27 . This high vaccination coverage could lead to much smaller outbreaks and potentially much weaker signals reflected on Google Trends. Consequently, other studies have found high correlation between monthly clinical case and Google Trends data over measles by summing up 3 countries’ Google Trends signals and cases for Italy, France, Germany, and Romania during 2013–2018 due to each country’s weak Google Trends signal 28 , 29 .

This study aimed to provide guidance for evaluating whether Google Trends can be applied to monitoring other diseases, such as measles. If Google Trends search data is found to be highly correlated with disease clinical case data in the context of a highly-vaccinated disease like measles, then previously published methods can be adapted to establish a pseudo-surveillance system for measles. We developed insights into what disease outbreak patterns are captured by Google Trends at both country and regional levels, how to better utilize these data, and limitations of using Google Trends to monitor disease outbreaks. We also share insights of which similarity measurements may be more suitable for this particular task. Popular performance measurements are adopted with further justification in this application area. However, those widely used performance measurements could lead to dramatically different conclusions 30 , 31 .

Correlation analysis of measles between Google Trends search data and clinical case data was performed to evaluate if Google Trends search data are highly correlated with clinical case data, even for highly vaccinated diseases like measles. If so, then the same methods from the previous study 26 can be easily adapted to other diseases to establish the pseudo-surveillance system. The analysis was performed at the country level across 29 EU/EEA Member States and the UK. Japan and Germany were investigated at the regional level. With limited clinical case data, only Google Trends search data of the top 10 countries with the largest number of measles cases from October 2022 to March 2023 were evaluated.

Monthly measles clinical case data for 29 EU/EEA Member States and the UK from 2016/04 to 2020/02 were collected from the European Centre for Disease Prevention and Control (ECDC) monthly measles and rubella monitoring reports 32 . Empty entries were filled with the floor of the average for previous and next months. Japan and Germany were selected for further investigation at the regional level, as the weekly case reports of those two countries at regional level were available. Weekly measles clinical case data in Germany from 2017 to 2019 were obtained from SurvStat database provided by Robert Koch Institut (RKI) 33 . Weekly measles clinical case data for Japan from 2017 to 2019 were gathered from the National Institute of Infectious Diseases (NIID) 34 .

Google Trends 35 search data reflects how a specific search interest varies for a region over time, ranging from 100 to 0%, scaled by the highest search number that a specific search interest ever generated within the chosen time period. Weekly or monthly data points are extracted if the chosen time period is shorter or longer than 5 years, respectively. The keyword “麻疹”, in Japanese was used for Japan, and “Measles” in boèth English, as well as translations into the first language of each European country using Google Translate, were used. The keyword “Measles”, in English, was used for the top 10 countries with the largest number of measles cases from October 2022 to March 2023.

Measurement

Both Pearson’s correlation coefficient (PCC) and Spearman’s correlation coefficient (SRCC) were calculated between Google Trends and clinical case data. PCC measures the linear correlation between two sets of data, while SRCC measures the rank correlation (i.e., the statistical dependence between the rankings of two variables). Both range from − 1 to 1, with 1 indicating perfect correlation, 0 indicating no correlation, and − 1 indicating perfect anti-correlation. PCC does not imply significance of SRCC (and vice versa) 36 . Results of both estimators with the statistical significance levels of 0.05 and 0.01 were listed, as both statistics have been used in previous studies 26 , 29 . The Python library package SciPy 37 , was used to perform the correlation analyses.

Outbreaks captured in Google Trends for high-income countries

The monthly number of measles cases for all 29 EU/EEA member states and the UK from 2016/04 to 2020/02 is shown in Fig. 1 . For illustration purposes, among 30 countries, the top 10 countries ranked by number of total cases showed clear acute outbreak patterns in Fig. 1 . Correlations between monthly Google Trends search and clinical case data of the top 10 member states and the UK by month from 2016/04 to 2020/02 are shown in Fig. 2 . The results for all countries are listed in Table 1 . Countries with blank results are due to: (1) The measurement is not statistically significant ( p -value \(\ge\) 0.05); (2) No search activities for the specified keyword were captured on Google Trends data during the selected time period. Google Trends with keywords in each country’s official language usually resulted in a higher correlation with clinical case data compared to keywords in English. A search with keywords combined in multiple languages does not necessarily result in a higher correlation.

Measles outbreaks were not captured on Google Trends for LMICs. The top 10 countries with the largest number of measles cases ranged from 68,473 (India) to 1769 (Nigeria) from October 2022 to March 2023 38 were investigated. Only India showed clear patterns on Google Trends.

The monthly number of measles cases for 29 EU/EEA member states and the UK from 2016/04 to 2020/02. Number of total cases are indicated after the country name in the legend. Plots of 10 countries with the most number of cases are shown in lower figures in various scales to show the outbreak trends.

Correlation between monthly Google Trends and clinical case data of top 10 EU/EEA member states and the UK by month from 2016/04 to 2020/02.

Accurate acute outbreaks captured in Google Trends at regional level

High correlations were found between weekly Google Trends search and clinical case data. Germany and Japan were investigated at regional level. For Germany, low correlations for either the Pearson correlation coefficient (PCC) (0.25) or the Spearman’s rank correlation coefficient (SRCC) (0.37) measurements were observed at the country level, as shown in Fig. 3 . At the regional level, two states were selected for illustration purposes. Lower Saxony was selected because it had the highest Google Search volumes compared to all other states. North Rhine-Westphalia was selected because it had the highest number of cases from 2017 to 2019. At the country level, the outbreak in 2018 was completely missed on Google Trends. However, it was well captured on Google Trends in regions where the outbreak occurred (e.g., North Rhine-Westphalia). Regions (e.g., Lower Saxony) without any outbreak in 2018 showed no activity on Google Trends as well. Similar observations were found in Japan. At the country level, both low correlations for PCC (0.33) and SRCC (0.37) measurements were observed from 2016 to 2019 as shown in Fig. 4 . In 2017, the outbreak was not captured on Google Trends for at the country level, but it was captured on Google Trends of Yamagata, where the outbreak occurred. In 2018, although Google Trends search and clinical case data aligned well, Google Trends of big cities (e.g., Tokyo, Kyoto) also captured search volume spikes, where no outbreak happened. The outbreak was mainly in Okinawa. In 2019, the amplitude of Google Trends signals was far lower than clinical case data. This is because several cases happened in multiple regions, adding up to a high number of weekly cases at the country level. Acute outbreaks (sudden large numbers of cases within a short period of time) were captured on Google Trends in Osaka. However, there were few cases circulating around during a long period of time in Tokyo, which did not trigger a high search volume spike pattern on Google Trends.

Correlation between weekly Google Trends search and clinical case data of Germany and regions with most Google searches and cases between 2017 to 2019.

Correlation between weekly Google Trends search and clinical case data of Japan and regions with most cases between 2017 to 2019.

The Pearson correlation coefficient more suitable than the Spearman’s rank correlation coefficient

The Pearson correlation coefficient (PCC) seems to be more suitable than Spearman’s rank correlation coefficient (SRCC) estimation for this task. For example, for Poland, as shown in Fig. 2 , using the keyword in English for Google Trends resulted in a pattern more similar to the clinical case data, leading to a higher PCC (0.77 vs. 0.32) and a lower SRCC (0.44 vs. 0.52) compared to using the “odra” keyword in Polish. For Belgium, the first spike in clinical data was completely missed in Google Trends, resulting in a low PCC (0.35), but a high SRCC (0.67). In Japan, as shown in Fig. 4 , Okinawa showed perfect correlation between Google Trends search and clinical case data. However, the SRCC only yielded a low value of 0.40, while the PCC showed 0.86.

Google Trends can complement existing surveillance systems for monitoring disease outbreaks in real-time. High correlations between Google Trends search and clinical case data were observed for measles. It is most suitable to monitor acute disease outbreaks at the regional level in high-income countries. Although these high-income countries usually have high-quality weekly case reports, we observed that weekly reports may be delayed for several weeks due to various reasons. On the other hand, Google Trends is able to provide weekly trends in real-time. It can also be used as a supplemental surveillance system for countries with limited sentinel network coverage.

Occasionally, a single keyword such as “measles” in the first language could be sufficient for identifying the clear outbreak patterns for measles on Google Trends in most countries. Adding the keyword “measles” in English may result in noisier data, which could lower the accuracy of monitoring outbreaks using Google Trends.

When estimating correlations between Google Trends search and clinical case data, the Pearson correlation coefficient seems to be more suitable than Spearman’s rank correlation coefficient for this particular task.

Previous studies have only investigated the correlations between clinical case data and Google Trends search data for measles at the country level 28 , 29 , 39 . For example, due to the weak signal from Google Trends data, Samaras and colleagues aggregated Google Trends data from three countries to evaluate the correlation with clinical case data 29 . In contrast, we evaluated correlations at the regional level and found that correlations between clinical case data and Google Trends were stronger at the regional level than the national level. Using this approach in developing a pseudo-surveillance system has greater potential to localize disease outbreaks.

Limitations

There are also limitations to using Google Trends to monitor disease outbreaks. At the country level, Google Trends does not work well in LMICs. This may be due to poor Internet infrastructure limiting Internet access, low education levels, or low healthcare coverages, limiting knowledge-seeking behaviors. In high-income countries, compared to acute outbreaks, Google Trends cannot capture prolonged outbreaks with very few cases (<10 cases/week) circulating around all the time, such as the outbreaks in Tokyo in 2019 shown in Fig. 4 . This may be due to the disease being around for too long but not widespread, causing people not to worry to continue to search. Also, local signals on Google Trends may not necessarily mean local outbreaks, such as the spikes on Google Trends of Tokyo and Osaka in 2018. This may be due to searches in big cities are coming from people like news staff, healthcare officials, or researchers, whose searches are not related to local outbreaks only. However, big cities usually have alternative existing surveillance systems to confirm whether there is a local outbreak. Google Trends data are sensitive to the selection of keywords. In this paper, we’ve only used one keyword to identify trends for our preliminary investigation, which could be more prone to false alerts triggered from news that may not relate to disease outbreaks.

This paper investigated the adaptation and feasibility of monitoring disease outbreaks using Google Trends data in real-time, especially for countries and diseases with limited or no sentinel network surveillance system. Using measles as an extreme case, which was much less widespread due to high vaccination coverage rates and early introduction (i.e., more than 60 years ago), Google Trends was found to be a potentially useful tool for monitoring of disease outbreaks at the regional level in developed countries. These results show promising potential for Google Trends data to be used in real-time disease surveillance for many diseases, even in challenging contexts. The Pearson correlation coefficient was more suitable than Spearman’s rank correlation coefficient with respect to evaluating correlations between clinical case data and Google Trends search data.

Data availability

The datasets generated and/or analyzed during the current study are publicly available at: Monthly measles and rubella monitoring report, https://www.ecdc.europa.eu/en/rubella/surveillance-and-disease-data/monthly-measles-rubella-monitoring-reports Notified measles cases in japan, https://www.niid.go.jp/niid/en/measles-e.html Google Trends, https://trends.google.com/trends/ Global measles outbreaks, https://www.cdc.gov/globalhealth/measles/data/global-measles-outbreaks.html Survstat@rki 2.0, https://www.rki.de/EN/Content/infections/epidemiology/SurvStat/survstat_node.html.

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Acknowledgements

This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

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D.W: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing—Original Draft Preparation, Visualization, Writing—Review & Editing; J.L.: Writing—Review & Editing; Y.C.: Conceptualization, Project Administration, Supervision, Writing—Review & Editing.

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Wang, D., Lang, J.C. & Chen, YH. Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study. Sci Rep 14 , 9470 (2024). https://doi.org/10.1038/s41598-024-60120-8

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DOI : https://doi.org/10.1038/s41598-024-60120-8

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We couldn't find what you are looking for, google trends: understanding the data..

How to interpret Trends results.

Sourcing Google Trends data.

Google Trends analyses a sample of Google web searches to determine how many searches were done over a certain period of time.

For example, if you’re doing a story about the Zika virus and you want to see if there was a recent uptick in searches on the topic, select  Past 90 days . Trends analyses a sample of all searches for Zika virus within those parameters.

Reading the Interest Over Time graph.

When you search for a term on Trends, you’ll see a graph showing the term’s popularity over time in (nearly) real time. Hovering your mouse over the graph reveals a number, which reflects how many searches have been done for the particular term relative to the total number of searches done on Google.

Numbers on the graph don't represent absolute search volume numbers, because the data is normalised and presented on a scale from 0-100, where each point on the graph is divided by the highest point, or 100. The numbers next to the search terms at the top of the graph are sums, or totals.

A line trending downward means that a search term's relative popularity is decreasing—not necessarily that the total number of searches for that term is decreasing, but that its popularity compared to other searches is shrinking.

Finding the most searched topic in every region or country.

When you search for multiple terms on Trends, you’ll see a comparative map showing which term or topic is most searched in each region. 

Step 1 Interest over time comparison. Let’s compare the search terms Zika virus and malaria. You’ll find that over time, malaria experiences a steady query rate  while Zika was barely searched for until a huge spike in January 2016.

Step 2 Compared breakdown by subregion: The colour intensity of each region represents the percentage of searches of the leading search term in that region. This example shows that Zika virus was a more popular search term in the Americas while malaria was relatively more popular in Asia.

Rising data.

At the bottom of your results page, the  Related queries  chart can show you the Top and Rising terms associated with any topic or trending story.  The  Rising  tab represents terms that were searched for with the term you entered and had the most significant growth in volume over the selected time period. You’ll see a percentage of the Rising term’s growth compared to the previous time period. If you see “Breakout” instead of a percentage, it means that the search term grew by more than 5000%.

The percentages are based on the percent increase in search interest for the selected time frame. If you’re looking at the last 7 days, the benchmark for the rise in searches  would be 7 days prior; if it was the last 30 days, the benchmark would be for the 30 days prior. The only exception is when viewing the full history (2004-Present), when the percentages are benchmarked at 2004.

Reading the Related searches chart.

Step 1 Click the dropdown to see Top terms.

Step 2 This table shows terms that are most frequently searched with the term you entered, in the same search session, with the same chosen category, country or region. If you didn’t choose a search term (and just chose a category or region), overall searches are displayed.

Data that is excluded.

Trends excludes certain data from your searches:

• Searches made by very few people:  Trends only analyses data for popular terms, so search terms with low volume appear as 0 for a given time period.
• Duplicate searches:  Trends eliminates repeated searches from the same user over a short period of time for better overall accuracy.
• Special characters:  Trends filters out queries with apostrophes and other special characters.

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Open Access

Peer-reviewed

Research Article

The Use of Google Trends in Health Care Research: A Systematic Review

Affiliation Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America

Affiliation Yale School of Medicine, New Haven, Connecticut, United States of America

Affiliation Yale School of Public Health, New Haven, Connecticut, United States of America

* E-mail: [email protected]

• Sudhakar V. Nuti, 
• Brian Wayda, 
• Isuru Ranasinghe, 
• Sisi Wang, 
• Rachel P. Dreyer, 
• Serene I. Chen, 
• Karthik Murugiah

• Published: October 22, 2014
• https://doi.org/10.1371/journal.pone.0109583
• Reader Comments

Google Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research.

Methods and Findings

PRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source.

Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.

Citation: Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. (2014) The Use of Google Trends in Health Care Research: A Systematic Review. PLoS ONE 9(10): e109583. https://doi.org/10.1371/journal.pone.0109583

Editor: Martin Voracek, University of Vienna, Austria

Received: May 23, 2014; Accepted: September 3, 2014; Published: October 22, 2014

Copyright: © 2014 Nuti et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

Funding: The authors have no funding or support to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

New tools are emerging to facilitate health care research in the Big Data era. One form of Big Data is that which accumulates in the course of Internet search activities. Internet search data may provide valuable insights into patterns of disease and population behavior. [1] In fact, the Institute of Medicine recognizes that the application of Internet data in health care research holds promise and may “complement and extend the data foundations that presently exist”. [2] An early and well-known example of utilizing Internet data in health has been the surveillance of influenza outbreaks with comparable accuracy to traditional methodologies. [3] .

One tool that allows users to interact with Internet search data is Google Trends, a free, publically accessible online portal of Google Inc. Google Trends analyzes a portion of the three billion daily Google Search searches and provides data on geospatial and temporal patterns in search volumes for user-specified terms. [4] Google Trends has been used in many research publications, but the range of applications and methods employed have not been reviewed. Furthermore, there are no guidance or agreed standards for the appropriate use of this tool. A critical appraisal of the existing literature would increase awareness of its potential uses in health care research and facilitate a better understanding of its strengths and weaknesses as a research tool.

Accordingly, we performed a systematic review of the health care literature using Google Trends. To characterize how researchers are using Google Trends, we classified studies by topic domain and study aim. We conducted a subanalysis of surveillance studies to further detail their methods and approach to validation. We also assessed the reproducibility of methods and created a checklist for investigators to improve the quality of studies using Google Trends. Finally, we address general limitations in using Google Trends for health care research.

Overview of Google Trends

Google Trends provides access to Internet search patterns by analyzing a portion of all web queries on the Google Search website and other affiliated Google sites. [5] A description of the user interface is shown in Figure S1 . Users are able to download the output of their searches to conduct further analyses.

The portal determines the proportion of searches for a user-specified term among all searches performed on Google Search. It then provides a relative search volume (RSV), which is the query share of a particular term for a given location and time period, normalized by the highest query share of that term over the time-series. [6] , [7] The user can specify the geographic area to study, whether a city, country, or the world; data is available for all countries worldwide. Furthermore, the user can choose a time period to study, ranging from January 2004 to present, divided by months or days. The user is also able to compare the RSV of up to five different search terms or the RSV of a particular search term between geographic areas and between time periods. In addition, the user can choose from 25 specific topic categories to restrict the search, each with multiple sub categories for >300 choices in total, such as “Health → Mental Health → Depression”.

With respect to search input, multiple terms could be searched in combination with “+” signs and terms can be excluded with “-” signs. Quotations can be used to specify exact search phrases. [8] .

Study Selection

The review was conducted in accordance with PRISMA guidelines. [9] We included all studies that used Google Trends to answer research questions within the domain of health care. After an initial review, we included letters because they contained substantial original content. We also included studies using Google Insights for Search, a similar tool to Google Trends that was merged into Google Trends in 2012 (hereafter we will refer to studies using Google Insights for Search as using Google Trends for ease of reading).

We excluded studies that primarily focused on Google Flu Trends, a separate tool to specifically track seasonal variation in influenza trends. This tool is distinct from Google Trends and is therefore beyond the scope of this review. We also excluded articles that had no substantial use of Google Trends.

Search Strategy

We identified relevant studies by searching Ovid MEDLINE (from inception to January 3, 2014) using a comprehensive search strategy. The list of subheadings (MeSH) and text words used in the search strategy for MEDLINE can be found in Appendix S1 . We only included studies of humans written in the English language, and identified 1249 potential articles for inclusion. Since PubMed contains articles from life science journals in addition to articles indexed in MEDLINE, we conducted a search of PubMed (from inception to January 3, 2014) using a similar search strategy, but excluding the articles already identified from MEDLINE. This search identified an additional 871 potential articles, for a total of 2120 potential articles.

Two reviewers (S.V.N. and K.M.) independently reviewed the titles and abstracts of retrieved publications, and 92 articles met our inclusion criteria for full text review. We then excluded 25 studies that did not utilize Google Trends or that met at least one of our exclusion criteria (See Figure 1 ). We also included 3 articles found from the review of references.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0109583.g001

The remaining 70 studies that met our inclusion criteria and did not meet our exclusion criteria form the studies included in this review. Data were abstracted from these studies using a standardized instrument, described below and in Table 1 . All extractions were performed by at least two of the authors, and disagreements were resolved by consensus. We did not pool the results due to the heterogeneity of the articles, but we provide summary statistics.

https://doi.org/10.1371/journal.pone.0109583.t001

Evaluation of studies

Article classification..

To characterize how researchers are using Google Trends, we created a general descriptive classification of the articles according to their topic domain and study aim using an iterative process. The research team first worked together to examine all of the articles and identify common themes among the articles. We then assigned each article to the themes that emerged. After this initial step, we reassessed these groupings, refining the categorical domains and reassigning articles as needed, to create a classification construct that best characterizes the articles in the review. All disagreements during this process were resolved by consensus. This resulted in four topic domains (infectious disease, mental health and substance use, other non-communicable diseases, general population behavior) and three study aims (causal inference, description, surveillance). Of note, to categorize study aim, we examined the primary aim of the study as stated by the authors in the introduction of the paper. The definitions of these categories are described in the results.

Variable Abstraction.

The variables extracted, along with the standard definitions and rationale for their selection, are listed in Table 1 . These pertain to each study’s purpose, methodology (search variables, search input, and type of analysis), primary findings, and citations accrued.

We defined whether an article was “reproducible” based on whether the authors provided a clear documentation of all fields modifiable by the user, namely location of search, time period of search, query category, and terms utilized, as well as the clear documentation of combination used and quotations used when applicable. Only articles that clearly provided each of these fields (or were deemed not applicable for a given field(s) with all other fields provided) were defined as reproducible. We defined “clear search input” as providing a clear use of quotations and/or combination when applicable (see Table 1 ).

Subanalysis of Surveillance Studies.

Following on the popularity of Google Flu Trends, there is particular interest in the potential use of Google Trends data to be operationalized as independent surveillance systems for other diseases. However, such surveillance systems require high standards of testing and validation before being deployed in the real world. Given these particular challenges, we performed a subanalysis of surveillance studies (as determined by study aim), abstracting additional information including the data sources used for validation, the strength of the relationship between predictions and external data, and other methodological characteristics listed in Table 1 and Table S1 . We only assessed if validation data was used in surveillance studies, but we did not assess the quality of the validation process or data.

Assessment of Bias.

Conventional tools to assess bias are largely limited to randomized trials and observational studies and are not readily applicable to studies using Google Trends data, which is observational in nature but does not involve individual research participants. [10] , [11] Therefore, we attempted to address the two primary sources of potential bias within these studies: the search strategy and the validation of surveillance studies. Search methodology may introduce bias, as the selection of terms and changes in search input can alter results. We therefore captured all aspects pertinent to search strategy, including the provision of rationale for search input, for each article. The data sources and methods for validating findings in surveillance studies are also sources for bias, which we assessed in our subanalysis. We assessed for publication bias by evaluating the number of studies with positive findings versus neutral/negative findings.

Study Sample

The 70 articles included in this systematic review are outlined in Table 2 . Overall, 92% were original articles; the remaining were letters. Among the articles identified, we observed a seven-fold increase in publications utilizing Google Trends from 2009 to 2013 ( Figure S2 ). Sixty-three percent of the articles chose to study areas outside of the United States alone. The median number (interquartile range) of article citations was 7 (1,16). The majority of studies (93%) presented positive findings with the tool compared to neutral/negative findings, indicating the possibility of publication bias.

https://doi.org/10.1371/journal.pone.0109583.t002

Classification of Published Google Trends Articles

Topic domain..

We classified articles by the primary topic addressed by each article. By consensus we identified four main topic domains: infectious diseases (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). The general population behavior category included all health-related behaviors excluding mental health and substance use.

There were three categories of study aim: causal inference (27%), description (39%), and surveillance (34%). We defined causal inference studies as those in which the primary aim was to evaluate a hypothesized causal relationship with Google Trends data. An example of a causal inference study is Ayers et al. (2014), who used search queries to explore the potential link between a public figure’s cancer diagnosis and population interest in primary cancer prevention. We defined descriptive studies as those that aimed to describe temporal or geographic trends and general relationships, without reference to a hypothesized causal relationship. An example of a descriptive study is Stein et al. (2013), who assessed public interest in LASIK surgery and how levels of interest have changed over time in the United States and other countries. A particular subset of descriptive studies were surveillance studies, which we defined as those in which the stated aim was to evaluate the use of Google Trends to predict or monitor real-world phenomena. An example of a surveillance study is Desai et al. (2012), who assessed whether Google search trends are appropriate for monitoring Norovirus disease.

Methodology of Published Google Trends Articles

Documentation of search strategy..

Table 3 summarizes the documentation of search strategy. Only 34% of articles documented the date the search was conducted.

https://doi.org/10.1371/journal.pone.0109583.t003

Of the variables that can be manipulated within the portal, within the methods section of the papers, 87% documented the location searched, 87% documented the time period searched, and only 19% clearly stated the query category used.

With respect to the search input, only 39% provided a clear search input. Excluding studies with only a single, one-word search term, which are not eligible for using quotations or combinations of terms, only 31% provided a clear search input. Of the articles eligible for using quotations (search terms with >1 word), 81% were unclear and 19% did not use quotations; none provided a clear use of quotations. Of the articles eligible for using a combination of terms (>1 search term used), 31% used a combination, 18% were unclear, and 51% used individual terms.

Reproducibility and Rationale.

Overall, only 7% of articles provided requisite documentation for a reproducible search strategy within their methods section; among original articles alone it was 8%.

In addition, we found that only 67% of articles provided a rationale for their search input.

Analytic Method.

Time trend analysis (comparisons across time periods) was used by 70% of the studies, cross-sectional analysis (comparisons across different locations at a single time period) by 11% of studies, and both by 19%. A variety of analytic methods were used in conjunction with Google Trends output data, including correlation, continuous density hidden Markov models, ANOVA, Box–Jenkins transfer function models, t-tests, autocorrelation, multivariable linear regression, time series analyses, wavelet power spectrum analysis, Cosinor analysis, and the Mann-Whitney test.

Subanalysis of Surveillance Articles and Validation

Among the 24 surveillance studies, 71% used time trend analysis, 25% cross-sectional analysis, and 4% both. Among articles using time trend analysis, 33% utilized lead-time analysis (using Google Trends data from a specific time point/interval to predict events occurring at a later time). Overall, 17% of studies used training/testing data sets and 13% had a time horizon (time period over which surveillance was assessed) <1 year. More detailed information can be found in Table S1 .

With respect to validation, 92% made comparisons against external datasets to validate the Google Trends output; the remaining 8% did not validate their findings. Examples of sources of comparison datasets include disease prevalence data from centers such as the United States Centers for Disease Control, drug revenues from shareholder reports of pharmaceutical companies, and unemployment rates from the Australian Bureau of Statistics.

There was a wide range of correlation statistics, from 0.04 to 0.98 ( Figure S3 ). Among the 15 papers that used Pearson product-moment correlation, 80% had at least one correlation statistic greater than 0.70.

Checklist for the Documentation of Google Trends Use

In view of the limitations of existing studies identified during this review, we developed a checklist to improve the quality and reproducibility of studies that use Google Trends in the future ( Table 4 ). This was created based directly on the variables that can be manipulated within the Google Trends portal, differences in which could provide differing results among researchers, and the need to provide search strategy rationale. A hypothetical example of a “well-documented” search strategy is included within Table 4 . Of note, we used brackets to separate the search input from the body text to ensure that the reader understands what was searched for with clear syntax; similar approaches of segregation might be used.

https://doi.org/10.1371/journal.pone.0109583.t004

In this systematic review of the use of Google Trends in healthcare research, we found that researchers are increasingly utilizing the tool in a diversity of areas in myriad ways; these articles are being widely cited. Furthermore, the majority of surveillance studies validated Google Trends output against external datasets and many had strong correlation statistics. However, the majority of studies lack thorough documentation of search methodologies, which precludes the reproducibility of results; less than 10% of articles are reproducible. In addition, search rationale is often not provided. Thus, while the data within Google Trends holds promise, significant variability and limitations remain around study quality and reliability.

The 70 papers included in our review reflected a wide variety of topics and uses. A large proportion of articles used Google Trends to investigate population behavior, which is a logical application of the tool given its basis in user searches. The large proportion of infectious disease articles may stem from the precedent set by Google Flu Trends. [3] Nearly equal numbers of studies used Google Trends for causal inference, surveillance, and description, demonstrating the ability to use the tool to answer a range of questions. There was an increase in publications over time, and the median citation rate (7 per article) is comparable to the average for all scientific articles (7.64 per article). [12] These observations suggest increasing awareness of and the leveraging of information from the tool. Locations studied using the tool were geographically widespread, particularly outside of the United States where conventional data collection may be challenging and resource intensive. Nevertheless, there is evidence of a positive results publication bias, which may be due to the novelty of the tool and authors not submitting and/or editors not accepting negative – and, therefore, perhaps uninteresting – results. [13] , [14] This publication bias also may be due to researchers retroactively constructing hypotheses about interesting findings after the results are known for a given Google Trends experiment, which can be fast and easy to conduct. [15] .

Despite the potential insights and research opportunities that Google Trends provides, many problems were observed with the documentation of methodology. Thorough documentation is necessary to ensure the reproducibility and replicability of the results, which are fundamental tenets of good science. [16] The inability to reproduce studies in the sciences has been well-documented, and it serves as a central problem to the utility and credibility of research. [17] , [18] Yet, in our study, only 7% of articles provided clear documentation of the necessary fields to be reproducible. This is especially salient for using Google Trends given the many search fields and multiple options available within each field. Researchers may not have known how to document their methods since this is still a nascent tool for research, without guidance or methodological standards for its use from either Google Inc. or the research community. Furthermore, there were particular issues with the clarity of search inputs. For example, it was often unclear whether quotations provided for a search term were actually used in the search input or were merely given to distinguish the term from the rest of the text. A potential reason for varying presentation of search input syntax may be that possible search syntaxes may have changed over time. [8] , [19] Nevertheless, the poor documentation of methods also raises larger questions about researchers using Google Trends without knowing the ways in which the tool can be operated.

Different selections of terms to address a common question with Google Trends can produce disparate results and conclusions, and providing the rationale behind these selections is necessary for a reader to better understand the study methods and to increase the face validity of the study. [20] Yet, studies often provided no rationale for their search input. For instance, we do not know why studies chose a given selection of terms or used a specific syntax. Furthermore, there are larger questions about the search strategy as a whole, such as why certain query categories and dates for searching were chosen. Nevertheless, certain studies demonstrated more thorough search strategies and strong rationales for search inputs, particularly accounting for the basis of Google Trends data in user searches. For instance, Desai et al. (2012) included potential misspellings of their search words to fully capture a specific search pattern. In addition, Cho et al. (2013) developed their search inputs by surveying their population of interest, in which they inquired about what search terms subjects would have used to search for influenza. Similar strategies could be adopted by future studies to ensure that their search terms accurately capture the outcome of interest. More guidance is needed by Google to assist researchers in how to produce an optimal search strategy to answer a given question.

Over 90% of surveillance studies compared Google Trends with established data sets, which were often trusted sources of surveillance data. A large number of correlation studies had moderate to strong strengths of association, which demonstrates the potential of Google Trends data to be used for the surveillance of health-related phenomena. For example, Jena et al. (2013) found a strong correlation between searches for HIV and US CDC HIV incidence rates, and were able to construct a model based on searches from years 2007–2008 to accurately predict state HIV incidence for 2009–2010. Moreover, Samaras et al. (2012) showed that Google Trends could have been used to forecast the peak of scarlet fever in the UK 5 weeks before its arrival. Although studies are promising, strong correlations alone do not support the use of Google Trends for surveillance, and further work is needed to substantiate the reliability and real world applicability of Google Trends as a tool to monitor health-related phenomena.

In light of our results, we have proposed a basic checklist for the documentation of Google Trends use. This checklist can serve as a baseline standard to ensure methodological understanding and reproducibility by researchers who choose to use the tool in the future.

While this checklist is a good step forward to improve the reproducibility of results by researchers, there are still larger limitations in the Google Trends tool itself. We cannot clearly ascertain user characteristics and intent from search data, which limits the ability to draw generalizable conclusions about population search behavior. In addition, Google Trends captures the search behavior of only a certain segment of the population – those with Internet access and using Google Search instead of other search engines. However, the major limitation of Google Trends is the lack of detailed information on the method by which Google generates this search data and the algorithms it employs to analyze it. Furthermore, temporal changes in the interface and capabilities of the Google Trends over time are not documented, which may lead to variation in the search output and therefore study findings.

Moving forward, several steps can be taken to improve the verification of Google Trends study results and the reliability of the tool for research, both on the part of the independent researcher and Google Inc. Researchers should strive to make the raw data they downloaded from the Google Trends available online (as Yang et al., 2010 did [21] ) and create an archive or screenshot of the website as they searched it (as Sueki et al., 2011 did [22] ) to provide transparency of their methodology and encourage open science with this open tool. Researchers could also evaluate the methods and results of others and themselves to check if there is consistency over time. We encourage Google Inc. to provide a chronology of important changes to Google Trends – in the past and to come – to put researchers’ methods and findings in context. Furthermore, if Google Trends continues to be used for research purposes, a discussion and collaboration between Google Inc. and the research community is necessary to create a set of best practices to ensure that the tool is being used responsibly and that its tremendous potential to derive meaningful insights from population search behavior could be fully harvested. While full transparency may not be possible due to commercial sensitivities, informed guidance is needed to ensure the conduct of ethical science. For example, Google Inc. could work together with groups of researchers to detail how to construct optimal queries to fully take advantage of the algorithms at work and to improve the tool to increase the quality of research. In addition, it is important to remember that these conclusions apply not only to Google Trends, but also other similar tools, which currently exist or will likely emerge from existing data sources, that are not intrinsically designed to be utilized for research. In a Big Data era where information and technologies, particularly those that are readily accessible to the public and research community, are growing, mindfulness must be paid to their application in scientific research and efforts must be made to ensure the conduct of good science. One must look no further than the recent controversy around the reliability of Google Flu Trends data to predict influenza incidence and the lack of transparency and inability to verify its results. [23] .

Our study has certain limitations. First, given the diversity of topics and uses, there are inherent challenges in the classification of articles. However, at least two independent abstractors reviewed each article and category of abstraction, and disagreements were resolved by group consensus. Second, there are no prior standards to evaluate literature from novel data sources such as Google Trends. Third, our assessment of Google Trends was based on the current syntactic possibilities, but they may have changed over time. [8] , [19] Conversely, this supports our concerns about undocumented changes to the tool. Finally, there is a possibility that we had an incomplete retrieval of Google Trends articles in our search strategy. However, we conducted an extensive, systematic search of two databases, in addition to reviewing article references, to capture as many articles as possible. Notably, our study focused on the evaluation of the use of Google Trends in research, and we refrained from making any commentary about the conclusions drawn by researchers in these studies. Further studies are needed to rigorously evaluate the interpretations of causal inference studies and the validity of Google Trends for surveillance.

Google Trends holds potential as a free, easily accessible means to access large population search data to derive meaningful insights about population behavior and its link to health and health care. However, to be reliably utilized as a research tool, it would have to be more transparent, which will increase the trustworthiness of both the results generated and its general applicability for health care research. Furthermore, researchers must make efforts to clearly state their rationale and document their experiments to ensure the reproducibility of results. The lessons gleaned from this review are also instructive for other tools not intrinsically designed for research that may emerge in an era of Big Data to ensure that they are used appropriately by the scientific community.

Supporting Information

Appendix s1..

MEDLINE Search Strategy.

https://doi.org/10.1371/journal.pone.0109583.s001

Surveillance Variable Abstraction Results.

https://doi.org/10.1371/journal.pone.0109583.s002

Google Trends Web Page Output. Screenshot of a Google Trends search output when queried for 3 terms: [“Google Trends”], [“Google Insights”], and [“Google Trends” + “Google Insights”]. We searched Worldwide, using all query categories, for the time period from January 2004 to March 2014 (site accessed: 3/17/14).

https://doi.org/10.1371/journal.pone.0109583.s003

Distribution of Articles Included in Our Review by Year of Publication. Notably, we did not include those articles published in 2014 (n = 5) in the figure, as they represent only part of that year.

https://doi.org/10.1371/journal.pone.0109583.s004

Forrest Plot of Measures of Association for Surveillance Studies Using Pearson’s Correlation. Plot of correlation statistics from each surveillance study that used Pearson’s correlation. For studies with multiple correlation statistics, each was plotted individually.

https://doi.org/10.1371/journal.pone.0109583.s005

Checklist S1.

PRISMA Checklist.

https://doi.org/10.1371/journal.pone.0109583.s006

Acknowledgments

We would like to thank Dr. Harlan M. Krumholz for his guidance with the manuscript.

Author Contributions

Conceived and designed the experiments: SVN KM IR BW. Performed the experiments: SVN KM IR BW SW RPD SIC. Analyzed the data: SVN KM IR BW. Contributed reagents/materials/analysis tools: SVN KM IR BW SW RPD SIC. Contributed to the writing of the manuscript: SVN KM IR BW SW RPD SIC.

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Google Trends Tool Review and Use Cases

By Jon Clark / July 01, 2020

Google Trends Background 

Google Trends is a keyword research tool that shows you keyword traffic over time in various regions and languages. The service was augmented by the addition of Google Insights for Search, which added a visual component to view keyword performance in countries, states, provinces, etc in the form of maps. Google Trends can be used to perform some basic research to inform your SEO (search engine optimization) and pay-per-click (PPC) strategies. 

Google Trends Features

Google Trends pulls its data from its own searches. While it’s not as robust as Ubersuggest or SEMrush , it does provide you indispensable information using an intuitive interface.

Google Trends lets you see how your keyword is performing in different countries around the world. You can also search by subregions as well. That means, for example, that you can look at keyword performance in America and its states.

You can look at your results over various periods of time, ranging from keyword performance within the hour to keyword performance from now all the way back to 2004.

You can see how your term does in different categories, such as art and entertainment or autos and vehicles.

You can see how your keyword performs on different APIs. They include: Google search, image search, news search, Google shopping, and Youtube search.

You can compare one keyword to multiple others to compare their traffic over time. 

Related Topics

Topics, or keywords, are what digital marketers search for so they can create more effective SEO or PPC campaigns. Using this data, an SEO or PPC media agency can craft copy that’s more attuned with that search engine algorithms consider to be high quality results. 

Related Queries

These are terms that people who searched for your term also searched for. They can be arranged by how they place in the top 100 most frequently searched terms. You can also arrange this data based on whether the term is now getting searched for more often. Terms that have made great gains are called “breakout”.

You can create a link to the various pieces of data Google Trends provides you with.

You can download your work as a CSV file, which allows you to view your work with programs that build tables. This includes Microsoft Excel and Google Sheets

Google Trends provides you with a link to embed your data using a high quality visualization

How Google Trends is Used

Google Trends is very easy to use. Just type in the term or topic you want to explore, and then you’ll get your results displayed using an intuitive interface. You can also add terms to compare your search results to just by adding them to the box marked “compare”. 

Google Trends Use Cases

Content Inspiration: By studying the related queries and focusing on those that are breakouts, you can be inspired to create new content that speaks to what people are clamoring for in the moment.

Comparing Topics: Being able to compare topics quickly can have many useful applications. When it comes to picking a title for your article, for example, you can see if people are more like to search for, “tips”, “tricks”, or, “hacks” and make your decision based on which term has the highest traffic.

Compelling Visuals: Google Trends isn’t complicated by an abundance of resources. It shows you how well your queries are performing over time or in different reasons, allowing for easy to interpret visualizations. These can be very useful for presentations because they’re so easy to read.

Google Trends Pricing

Google Trends is free!

Bottom Line on Google Trends

Google Trends may not be the most robust keyword research tool, so it won’t be relied on by an SEO agency or PPC media agency. B ut you don’t always need the most robust research tool! In fact, an over abundance of options can sometimes lead to complications. That’s why Google Trends is ideal for simple tasks like picking the right keyword for a title, inspiring ideas for content, or for creating visualizations for presentations. 

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Google Gives 5 SEO Insights On Google Trends

Google shares five practical insights about Google Trends that are useful for SEO and debugging search traffic issues

Google published a video that disclosed five insights about Google Trends that could be helpful for SEO, topic research and debugging issues with search rankings. The video was hosted by Daniel Waisberg, a Search Advocate at Google.

1. What Does Google Trends Offer?

Google Trends is an official tool created by Google that shows a representation of how often people search with certain keyword phrases and how those searches have changed over time. It’s not only helpful for discovering time-based changes in search queries but it also segments queries by geographic popularity which is useful for learning who to focus content for (or even to learn what geographic areas may be best to get links from).

This kind of information is invaluable for debugging why a site may have issues with organic traffic as it can show seasonal and consumer trends.

2. Google Trends Only Uses A Sample Of Data

An important fact about Google Trends that Waisberg shared is that the data that Google Trends reports on is based on a statistically significant but random sample of actual search queries.

“Google Trends is a tool that provides a random sample of aggregated, anonymized and categorized Google searches.”

This does not mean that the data is less accurate. The phrase statistically significant means that the data is representative of the actual search queries.

The reason Google uses a sample is that they have an enormous amount of data and it’s simply faster to work with samples that are representative of actual trends.

3. Google Cleans Noise In The Trends Data

Daniel Waisberg also said that Google cleans the data to remove noise and data that relates to user privacy.

“The search query data is processed to remove noise in the data and also to remove anything that might compromise a user’s privacy.”

An example of private data that is removed is the full names of people. An example of “noise” in the data are search queries made by the same person over and over, using the example of a trivial search for how to boil eggs that a person makes every morning.

That last one, about people repeating a search query is interesting because back in the early days of SEO, before Google Trends existed, SEOs used a public keyword volume tool by Overture (owned by Yahoo). Some SEOs poisoned the data by making thousands of searches for keyword phrases that were rarely queried by users, inflating the query volume, so that competitors would focus on optimizing on the useless keywords.

4. Google Normalizes Google Trends Data?

Google doesn’t show actual search query volume like a million queries per day for one query and 200,000 queries per day for another. Instead Google will select the point where a keyword phrase is searched the most and use that as the 100% mark and then adjust the Google Trends graph to percentages that are relative to that high point. So if the most searches a query gets in a day is 1 million, then a day in which it gets searched 500,000 times will be represented on the graph as 50%. This is what it means that Google Trends data is normalized.

5. Explore Search Queries And Topics

SEOs have focused on optimizing for keywords for over 25 years. But Google has long moved beyond keywords and has been labeling documents by the topics and even by queries they are relevant to (which also relates more to topics than keywords).

That’s why in my opinion one of the most useful offerings is the ability to explore the topic that’s related to the entity of the search query. Exploring the topic shows the query volume of all the related keywords.

The “explore by topic” tool arguably offers a more accurate idea of how popular a topic is, which is important because Google’s algorithms, machine learning systems, and AI models create representations of content at the sentence, paragraph and document level, representations that correspond to topics. I believe that’s what is one of the things referred to when Googlers talk about Core Topicality Systems .

Waisberg explained:

“Now, back to the Explore page. You’ll notice that, sometimes, in addition to a search term, you get an option to choose a topic. For example, when you type “cappuccino,” you can choose either the search term exactly matching “cappuccino” or the “cappuccino coffee drink” topic, which is the group of search terms that relate to that entity. These will include the exact term as well as misspellings. The topic also includes acronyms, and it covers all languages, which can be very useful, especially when looking at global data. Using topics, you also avoid including terms that are unrelated to your interests. For example, if you’re looking at the trends for the company Alphabet, you might want to choose the Alphabet Inc company topic. If you just type “alphabet,” the trends will also include a lot of other meanings, as you can see in this example.”

Related: 12 Ways to Use Google Trends

The Big Picture

One of the interesting facts revealed in this video is that Google isn’t showing normalized actual search trends, that it’s showing a normalized “statistically significant” sample of the actual search trends. A statistically significant sample is one in which random chance is not a factor and thus represents the actual search trends.

The other noteworthy takeaway is the reminder that Google Trends is useful for exploring topics , which in my opinion is far more useful than Google Suggest and People Also Ask (PAA) data.

I have seen evidence that slavish optimization with Google Suggest and PAA data can make a website appear to be optimizing for search engines and not for people, which is something that Google explicitly cautions against. Those who were hit by the recent Google Updates should think hard about the implications of what their SEO practices in relation to keywords.

Exploring and optimizing with topics won’t behind statistical footprints of optimizing for search engines because the authenticity of content based on topics will always shine through.

Watch the Google Trends video:

Intro to Google Trends data

Featured Image by Shutterstock/Luis Molinero

I have 25 years hands-on experience in SEO, evolving along with the search engines by keeping up with the latest ...

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FAQ about Google Trends data

Standard > Trends > FAQ about Google Trends data

Google Trends provides access to a largely unfiltered sample of actual search requests made to Google. It’s anonymized (no one is personally identified), categorized (determining the topic for a search query) and aggregated (grouped together). This allows us to display interest in a particular topic from around the globe or down to city-level geography.

What samples are provided?

There are two samples of Google Trends data that can be accessed:

Real-time data is a sample covering the last seven days.

Non-realtime data is a separate sample from real-time data and goes as far back as 2004 and up to 72 hours before your search.

How is a sample of searches representative?

While only a sample of Google searches are used in Google Trends, this is sufficient because we handle billions of searches per day. Providing access to the entire data set would be too large to process quickly. By sampling data, we can look at a dataset representative of all Google searches, while finding insights that can be processed within minutes of an event happening in the real world.

How is Google Trends data normalized?

Google Trends normalizes search data to make comparisons between terms easier. Search results are normalized to the time and location of a query by the following process:

Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity. Otherwise, places with the most search volume would always be ranked highest.

The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics.

Different regions that show the same search interest for a term don't always have the same total search volumes.

What searches are included in Google Trends?

Google Trends data reflects searches people make on Google every day, but it can also reflect irregular search activity, such as automated searches or queries that may be associated with attempts to spam our search results.

While we have mechanisms in place to detect and filter irregular activity, these searches may be retained in Google Trends as a security measure: filtering them from Google Trends would help those issuing such queries to understand we’ve identified them. This would then make it harder to keep such activity filtered out from other Google Search products where high-fidelity search data is critical. Given this, those relying on Google Trends data should understand that it’s not a perfect mirror of search activity.

Google Trends does filter out some types of searches, such as:

Searches made by very few people: Trends only shows data for popular terms, so search terms with low volume appear as "0"

Duplicate searches: Trends eliminates repeated searches from the same person over a short period of time.

Special characters: Trends filters out queries with apostrophes and other special characters.

Is Google Trends the same as polling data?

Google Trends is not a scientific poll and shouldn’t be confused with polling data. It merely reflects the search interest in particular topics. A spike in a particular topic does not reflect that a topic is somehow “popular” or “winning,” only that for some unspecified reason, there appear to be many users performing a search about a topic. Google Trends data should always be considered as one data point among others before drawing conclusions.

How can I better make use of and interpret Google Trends data?

This post from Google News Lab explains more about how Google Trends works and ways people might appropriately make use of the data.

How does trends data shared by Google News Lab differ from Google Trends?

For major events, the Google News Lab may share trends data ( such as via Twitter ) that is not accessible via the public Google Trends tool. We do monitor such data for evidence of irregular activity. However, as with regular Google Trends data, it is not scientific and might not be a perfect mirror of search activity.

How does Google Trends differ from Autocomplete?

Autocomplete is a feature within Google Search designed to make it faster to complete searches that you’re beginning to type. The predictions come from real searches that happen on Google and show common and trending ones relevant to the characters that are entered and also related to your location and previous searches.

Unlike Google Trends, Autocomplete is subject to Google’s removal policies as well as algorithmic filtering designed to try to catch policy-violating predictions and not show them. Because of this, Autocomplete should not be taken as always reflecting the most popular search terms related to a topic.

How does Google Trends differ from AdWords search data?

The AdWords search terms report is meant for insights into monthly and average search volumes, specifically for advertisers, while Google Trends is designed to dig further into more granular data in real time.

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Omni Hotels boosts conversions 4X by ditching cookies for Display & Video 360’s PAIR

From its roots in grand historic hotels to its collection of modern resort destinations, Omni Hotels & Resorts has been shaping the hospitality landscape for decades. With over 40 locations spanning across North America, Omni has continued to build upon its rich legacy that blends time-honored elegance with personalized experiences, offering guests a taste of genuine luxury. To navigate the privacy-focused landscape, Omni partnered with PMG, MiQ, and LiveRamp, adopting Google's Display & Video 360 Publisher Advertiser Identity Reconciliation (PAIR) solution to deliver relevant ads without compromising user data. This resulted in a remarkable 4X increase in ad conversion rates compared to traditional cookie-based methods, demonstrating success in delivering relevant experiences while respecting user privacy.

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Google Trends in Infodemiology and Infoveillance: Methodology Framework

Amaryllis mavragani.

1 Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom

Gabriela Ochoa

Associated data.

Google Trends categories.

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.

Introduction

The use of internet data has become an integral part of health informatics over the past decade, with online sources becoming increasingly available and providing data that can be useful in analyzing and predicting human behavior. This use of the internet has formed two new concepts: “Infodemiology,” first defined by Eysenbach as “the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy” [ 1 ], and “Infoveillance,” defined as “the longitudinal tracking of infodemiology metrics for surveillance and trend analysis” [ 2 ].

The main limitation of validating this line of research is the general lack of openness and availability of official health data. Data collection and analysis of official health data on disease occurrence and prevalence involve several health officials and can even take years until the relevant data are available. This means that data cannot be accessed in real time, which is crucial in health assessment. In several countries, official health data are not publicly available, and even in countries where data are available, they usually consist of large time-interval data (eg, annual data), which makes the analysis and forecasting of diseases and outbreaks more difficult.

Nevertheless, data from several online sources are being widely used to monitor disease outbreaks and occurrence, mainly from Google [ 3 - 7 ] and social media [ 8 - 12 ]. Twitter has become increasingly popular over the past few years [ 13 - 19 ], while several other studies have combined data from different online sources such as Facebook and Twitter [ 20 ] or Google, Twitter, and electronic health records [ 21 ].

Currently, the most popular tool in addressing health issues and topics with the use of internet data is Google Trends [ 22 ], an open online tool that provides both real-time and archived information on Google queries from 2004 on. The main advantage of Google Trends is that it uses the revealed and not stated users’ preferences [ 23 ]; therefore, we can obtain information that would be otherwise difficult or impossible to collect. In addition, as data are available in real time, it solves issues that arise with traditional, time-consuming survey methods. Another advantage is that, as Web searches are performed anonymously, it enables the analysis and forecasting of sensitive diseases and topics, such as AIDS [ 24 ], mental illnesses and suicide [ 25 - 27 ], and illegal drugs [ 28 , 29 ].

Despite the limitations of data from traditional sources and owing to the fact that online data have shown to be valuable in predictions, the combination of traditional data and Web-based data should be explored, as the results could provide valid and interesting results. Over the past few years, the diversity of online sources used in addressing infodemiology topics is increasing. Indicative recent publications of online sources and combinations of sources are presented in Table 1 .

Recent indicative infodemiology studies.

As discussed above, many studies have used Google Trends data to analyze online behavior toward health topics and to forecast prevalence of diseases. However, the literature lacks a methodology framework that provides a concise overview and detailed guidance for future researchers. We believe such a framework is imperative, as the analysis of online data is based on empirical relationships, and thus, a solid methodological basis of any Google Trends study is crucial for ensuring the value and validity of the results.

We proceed in a step-by-step manner to develop the methodology framework that should be followed when using Google Trends in infodemiology. First, we provide an overview of how the data are retrieved and adjusted along with the available features, followed by the methodology framework for choosing the appropriate keyword(s), region(s), period, and category. Finally, the results are discussed, along with the limitations of the tool and suggestions for future research.

Methodology Framework

Data overview.

Google Trends is an open online tool that provides information on what was and is trending, based on actual users’ Google queries. It offers a variety of choices, such as Trending Searches, Year in Search, and Explore. Table 2 describes the features offered by Google Trends and their respective descriptions.

Google Trends Features and Descriptions.

When using Google Trends for research, data are retrieved from the “Explore” feature, which allows download of real-time data from the last week and archived data for specific keywords and topics from January 2004 up to 36 hours before the search is conducted. The data are retrieved directly from the Google Trends Explore page in .csv format after the examined keyword(s) is entered and the region, period, and category are selected. By default, the period is set to “Worldwide,” the time frame is set to “past 12 months,” and the category is set to “All categories.”

The data are normalized over the selected time frame, and the adjustment is reported by Google as follows:

Search results are proportionate to the time and location of a query by the following process: Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity. Otherwise, places with the most search volume would always be ranked highest. The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics. Different regions that show the same search interest for a term don't always have the same total search volumes [ 50 ]

The normalization of data indicates that the values vary from 0 to 100. The value 0 does not necessarily indicate no searches, but rather indicates very low search volumes that are not included in the results. The adjustment process also excludes queries that are made over a short time frame from the same internet protocol address and queries that contain special characters. Google does not have a filter for controversial topics, but it excludes related search terms that are sexual. However, it allows retrieval of queries’ normalized hits for any keyword entered, independent of filters.

Google Trends allows one to explore the online interest in one term or the comparison of the online interest for up to five terms. It allows a variety of combinations to compare different terms and regions as follows:

Graphs of the variations in the online interest for the examined terms over the selected time frame in Google Trends.

• For the same term in different regions over the same period, such as for “Tuberculosis” in the United States and United Kingdom from March 24, 2007, to April 7, 2011 ( Figure 1 b)
• For different terms (up to five) in the same region for the same period, such as for the terms “Chlamydia,” “Tuberculosis,” and “Syphilis” in Australia from October 5, 2012, to December 18, 2012 ( Figure 1 c)
• For different terms (up to five) for different regions over the same period, such as comparing the term “Asthma” in the United States, “AIDS” in the United Kingdom, and “Measles” in Canada from June 1, 2017, to July 15, 2018 ( Figure 1 d)

When the term(s), region(s), period(s), and category are defined, the outputs are a graph of the variations of all examined terms in the online interest over the selected time frame ( Figure 1 ) and their respective heat maps, which are presented separately for all examined regions ( Figure 2 ); all datasets can be downloaded in .csv format.

Heat map for (a) “Asthma” in the United States from Jan 2004 to Dec 2014; (b) “Tuberculosis” in the United States and United Kingdom from March 24, 2007, to April 7, 2011; (c) “Chlamydia,” “Tuberculosis,” and “Syphilis” in Australia from Oct 5, 2012, to Dec 18, 2012; (d) “Asthma” in the United States, “AIDS” in the United Kingdom, and “Measles” in Canada from June 1, 2017, to July 15, 2018.

Apart from the graph, the .csv with the relative search volumes, and the interest heat maps, Google Trends also shows and allows one to download .csv files of (1) the “Top related queries”, defined as “Top searches are terms that are most frequently searched with the term you entered in the same search session, within the chosen category, country, or region” ( Figure 3 a); (2) the “Rising related queries”, defined as "terms that were searched for with the keyword you entered...which had the most significant growth in volume in the requested time period” ( Figure 3 b); (3) the “Top Related Topics” ( Figure 3 c); and (4) the “Rising Related Topics” ( Figure 3 d).

Google Trends’ (a) top related queries, (b) rising related topics, (c) top related topics, and (d) rising related queries for “Asthma” in the United States from Jan 1, 2004, to Dec 31, 2014.

Keyword Selection

The selection of the correct keyword(s) when examining online queries is key for valid results [ 51 ]. Thus, many factors should be taken into consideration when using Google Trends data in order to ensure a valid analysis.

Google Trends is not case sensitive, but it takes into account accents, plural or singular forms, and spelling mistakes. Therefore, whatever the choice of keywords or combination of keywords, parts of the respective queries will not be considered for further analysis.

To partly overcome this limitation, the “+” feature can be used to include the most commonly encountered misspellings, which are selected and entered manually; however, we should keep in mind that some results will always be missing, as all possible spelling variations cannot be included. In addition, incorrect spellings of some words could be used even more often than the correct one, in which case, the analysis will not be trivial. However, in most of the cases, the correct spelling is the most commonly used, and therefore, the analysis can proceed as usual. For example, gonorrhea is often misspelled, mainly as “Gonorrea,” which is also the Spanish term for the disease. As depicted in Figure 4 a, both terms have significantly high volumes. Therefore, to include more results, both terms could be entered as the search term by using the “+” feature ( Figure 4 b). In this way, all results including the correct and the incorrect spellings are aggregated in the results. Note that this is not limited to only two terms; the “+” feature can be used for multiple keywords or for results in multiple languages in a region.

Use of the “+” feature for including misspelled terms for (a) "Gonorrhea" compared to "Gonorrea"; (b) both terms by using the “+” feature.

In the case of accents, before choosing the keywords to be examined, the variations in interest between the terms with and those without accents and special characters should be explored. For example, measles translates into “Sarampión,” “ošpice,” “mässling,” and “Ιλαρά” in Spanish, Slovenian, Swedish, and Greek, respectively. As depicted in Figure 5 , in Spanish and Greek, the term without the accent is searched for in higher volumes; in Slovenian, the term with the accent is mostly used; and in Swedish, the term without the accent is almost nonexistent. Thus, in Greek searches, the term without accent should be selected, in Slovenian and Swedish searches, terms with accents should be used, while for Spanish, as both terms yield significant results, either both terms using the “+” feature or the term without the accent should be selected.

Selection of the correct keyword for measles based on the use of accents in the respective translated terms in (a) Spanish, (b) Slovenian, (c) Swedish, and (d) Greek.

Another important aspect is the use of quotation marks when selecting the keyword. This obviously applies only to keywords with two or more words. For example, breast cancer can be searched online by using or not using quotes. To elaborate, the term “breast cancer” without quotes will yield results that include the words “breast” and “cancer” in any possible combination and order; for example, keywords “breast cancer screening” and “breast and colon cancer” are both included in the results. However, when using quotes, the term “breast cancer” is included as is; for example, “breast cancer screening,” “living with breast cancer,” and “breast cancer patient.” As shown in Figure 6 a, the results are almost identical in this case. However, this is not always the case. As depicted in Figure 6 b, this is clearly different for “HIV test.” When searching for HIV test with and without quotes, the results differ in volumes of searches, despite the trend being very similar but not exactly the same.

Differences in results with and without quotation marks for (a) “Breast Cancer” and (b) “HIV test.”.

Finally, when researching with Google Trends, the options of “search term” and “disease” (or “topic”) are available when entering a keyword. Although the “search term” gives results for all keywords that include the selected term, “disease” includes various keywords that fall within the category, or, as Google describes it, “topics are a group of terms that share the same concept in any language.”

Therefore, it is imperative that keyword selection is conducted with caution and that the available options and features are carefully explored and analyzed. This will ensure validity of the results.

Region Selection

The next step is to select the geographical region for which query data are retrieved. The first level of categorization allows data download for the online interest of one or more terms worldwide or by country. The list available includes all countries, in most of which interest in smaller regions can be explored.

For example, in the United States, it is possible to compare results even at metropolitan and city levels. Figure 7 a shows the regional online interest in the term “Flu” worldwide, where the United States is the country with the highest online interest in the examined term, followed by the rest of the 33 countries in which the examined term is most popular. Figure 7 b shows the heat map of the interest by state in the United States in the term “Flu” over the past 5 years; either as a new independent search or by clicking on the country “USA” in the worldwide map. As shown in the right bottom corner of Figure 7 , Google Trends provides the relative interest for all 50 US states plus Washington DC.

Online interest in the term “Flu” over the past 5 years (a) worldwide and (b) in the United States.

In the case of the United States, it is possible to examine the online interest by metropolitan area, as depicted in Figure 8 with the examples of California, Texas, New York, and Florida. The option for examining the online interest at the metropolitan level is not available for all countries, where from the state (or county) level, the interest changes directly to the city level. This includes fewer cities than regions with available metropolitan area data, as, for example, in countries with very large populations like India ( Figure 9 e) or with smaller populations like Greece ( Figure 9 f).

Regional online interest in the term “Flu” at metropolitan level over the past 5 years in (a) California, (b) Texas, (c) New York, and (d) Florida.

Regional online interest in the term “Flu” at city level over the past 5 years in (a) Los Angeles, (b) Dallas, (c) New York, (d) Miami, (e) India, and (f) Greece.

Figure 9 depicts the online interest by city in the selected metropolitan areas of Los Angeles in California, Dallas in Texas, New York in New York, and Miami in Florida.

At metropolitan level, by selecting the “include low search volume regions,” the total of the included cities is 123 in Los Angeles, 67 in Texas, 110 New York, and 50 in Miami, while in India and Greece, the number of cities remains 7 and 2, respectively.

Period Selection

As the data are normalized over the selected period, the time frame for which Google Trends data are retrieved is crucial for the validity of the results. The selection of the examined time frame is one of the most common mistakes in Google Trends research. The main guideline is that the period selected for Google data should be exactly the same as the one for which official data are available and will be examined. For example, if monthly (or yearly) official data from January 2004 to December 2014 are available, then the selected period for retrieving Google Trends data should be January 2004 to December 2014. Neither 15 datasets for each individual year nor a random number of datasets arbitrarily chosen should be used; a single dataset should be compiled including the months from January 2004 to December 2014. Note that data may slightly vary depending on the time of retrieval; thus, the date and time of downloading must be reported.

Depending on the time frame, the interval for which data are available varies significantly ( Table 3 ), which includes the data intervals for the preselected time frames in Google Trends. Note that the default selection is 12 months.

Data intervals and number of observations for the default options in period selection.

The time frame can be customized at will; for example, March 24, 2007, to November 6, 2013 ( Figure 10 a). Furthermore, there is an option to select the exact hours for which data are retrieved, but only over the past week; for example, from February 11, 4 am, to February 15, 5 pm ( Figure 10 b).

Customized time range (a) from archive and (b) over the past week.

Finally, an important detail in the selection of the time frame is when the data retrieval changes from monthly to weekly and weekly to daily. For example, from April 28, 2013, to June 30, 2018, the data are retrieved in weekly intervals, while from April 27, 2013, to June 30, 2018, the data are retrieved in monthly intervals. Hence, the data from monthly to weekly changes in (roughly) 5 years and 2 months. For daily data, we observe that, for example, from October 4, 2017, to June 30, 2018, the data are retrieved in daily intervals, while from October 3, 2017, to June 30, 2018, the data are retrieved in weekly intervals; as such, the data interval changes from daily to weekly in (roughly) 10 months.

Search Categories

When exploring the online interest, the selected term can be analyzed based on a selected category. This feature is important to eliminate noisy data, especially in cases where the same word is used or can be attributed to different meanings or events. For example, the terms “yes” and “no” are very commonly searched for, so, when aiming at predicting the results of a referendum race, the search must be limited to the category “Politics” or “Campaign and elections” in order to retrieve the data that are attributed to the event. However, selecting a category is not required when the keyword searched is specific and not related to other words, meanings, and events.

The available categories are listed in Table A1 of Multimedia Appendix 1 . Note that most of these categories have subcategories, which, in turn, have other subcategories, allowing the available categories to be as broad or as narrow as required.

In this paper, we focus on the category of “Health” (first level of categorization). The main available subcategories (second level of categorization) of “Health” along with all available subcategories (third and fourth levels) are presented in Table A2 of Multimedia Appendix 1 .

Finally, another feature is the type of search conducted when entering a keyword, which consists of the options of “Web Search,” “Image Search,” “News Search,”“Google Shopping,” and “YouTube Search.” Apart from very specific cases, the “Web Search,” which is also the default option, should be selected.

Over the past decade, Web-based data are used extensively in digital epidemiology, with online sources playing a central role in health informatics [ 1 , 2 , 52 ]. Digital disease detection [ 53 ] consists of detecting, analyzing, and predicting disease occurrence and spread, and several types of online sources are used, including mainly digital platforms [ 54 , 55 ]. When addressing infodemiology topics, a concept first introduced by Eysenbach [ 1 ], Google Trends is an important tool, and research on the subject is constantly expanding [ 56 ]. Most studies on Google Trends research are in health and medicine, focusing mainly on the surveillance and analysis of health topics and the forecasting of diseases, outbreaks, and epidemics. As Google Trends is open and user friendly, it is accessed and used by several researchers, even those who are not strictly related to the field of big data, but use it as a means of exploring behavioral variations toward selected topics. The latter has resulted in differences in methodologies followed, which, at times, involve mistakes.

Despite the large number of studies in this line of research, there was a lack of a methodology framework that should be followed. This has produced differences in presentation, and, more importantly, in crucial mistakes that compromise the validity of the results. In this article, we provided a concise overview of the how the tool works and proposed a step-by-step methodology (ie, the four steps of selecting the correct/appropriate keyword, region, period, and category) to ensure the validity of the results in Google Trends research. We also included research examples to provide guidance not only to the experienced eye, but also to new researchers.

As is evident by the findings of this study, there are several limitations to the use of Google Trends data. First, despite the evident potential that Google data have to offer in epidemiology and disease surveillance, there have been some issues in the past, where online search traffic data at some point failed to accurately predict disease spreading, as in the case of Google Flu Trends [ 57 ], a Google tool for the surveillance of influenza-like illness (the flu) that is no longer available. Regardless, Google Flu Trends has been accurate in the past in predicting the spread of flu, as suggested by several studies and reports [ 58 - 60 ].

The latter could be partly attributed to the fact that, when researching with Google Trends, the sample is unknown and it cannot be shown to be representative. Despite this and considering the increasing internet penetration, previous studies have suggested that Web-based data have been empirically shown to provide valuable and valid results in exploring and predicting behavior and are correlated with actual data [ 61 - 66 ]. However, recent research has suggested that online queries do not provide valid results in regions with low internet penetration or low scorings in freedom of speech [ 67 ].

Furthermore, the data that are retrieved are normalized over the selected period; thus, the exact volumes of queries are not known, limiting the way that the data can be processed and analysis can be performed. Therefore, the data should be analyzed in the appropriate way, and the results should be carefully interpreted.

In addition, the selection of keyword(s) plays a very important role in ensuring the validity of the results. In some cases, the noisy data (ie, queries not attributed to the examined term) must be excluded, which are not always trivial. This can be partly overcome by selecting a specific category, which always bares the risk of excluding results that are needed for analysis.

The analysis of Google Trends data has several other limitations, as examining Web data can bear threats to validity. Careful analysis should be performed to ensure that news reporting and sudden events do not compromise the validity of the results. In addition, as the sample is unknown, several other demographic factors such as age and sex cannot be included in the analysis.

Finally, as this field of research is relatively new, there is no standard way of reporting, resulting in the same meaning of different terms, different meanings of the same term, and different abbreviations. For example, Google Trends data are referred to as relative search volumes, search volumes, online queries, online search traffic data, normalized hits, and other terms. Thus, future research should focus on developing specific coding for Google Trends research, so that a unified way of reporting is followed by all researchers in the field.

In the era of big data, the analysis of Google queries has become a valuable tool for researchers to explore and predict human behavior, as it has been suggested that online data are correlated with actual health data. The methodology framework proposed in this article for researching with Google Trends is much needed to provide guidance for using Google Trends data in health assessment, and, more importantly, to help researchers and health officials and organizations avoid common mistakes that compromise the validity of the results. As research on the subject is expanding, future work should include the coding in Google Trends research and extend this framework along with changes in the tool and the analysis methods.

Multimedia Appendix 1

Conflicts of Interest: None declared.

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