As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

Based on a survey conducted in 2019 among internet users in the United States, the majority of adults (36 percent) admitted they would switch search engines if it meant getting better quality results. Furthermore, 33.7 percent stated that knowing their data was not being collected by a platform would also encourage them to make the switch. Other factors listed included 'having fewer ads' and a well designed interface. Overall, there was a noticeable lean toward search result quality and data privacy when it came to search engine selection.

Google leads despite user preference for increased privacy

Despite a strong consumer call for data protection, Google topped the list when it came to search engines with 93 percent of Americans surveyed reporting to having used the popular search giant at some point during the past 4 weeks. In comparison, the second most popular platform Yahoo! had only been used by 31 percent of those surveyed. Meanwhile DuckDuckGo, the search engine most known for protecting user data and search history had only been used by 8 percent. Mobile search figures lean even more in Google's favor. Here, a similar share (93 percent) of the market as of January 2021 belonged to Google, while approximately 3 percent was held by DuckDuckGo.

Growth expected for search advertising

With search engines playing a significant role in internet use be it on desktop or mobile, companies and search platforms alike are seeing an increased opportunity in the field of search engine advertising. Nationwide spend in the industry reached an impressive 58.2 billion U.S. dollars in 2020, and was forecast to further rise to 66.2 billion within the following year.

In January 2025, Google remained by far the most popular search engine in the UK, holding a market share of ***** percent across all devices. That month, Bing had a market share of approximately **** percent in second place, followed by Yahoo! with approximately **** percent. The EU vs Google Despite Google’s dominance of the search engine market, maintaining its position at the top has not been a smooth ride. Google’s market share saw a decline in the summer of 2018, plummeting to an all-time-low in July. The search engine experienced a similar dip in June and July 2017. These two low points coincided with the European Commission’s antitrust charges against the company, both of which were unprecedented in the now decade-long duel between both parties. As skepticism towards search engine platforms grows in line with public concern regarding censorship and data privacy, alternative services like Duckduckgo offer users both information protection and unfiltered results. Despite this, it still held less than *** percent of the industry’s market share as of June 2021. Perception of fake news in the UK According to a questionnaire conducted in the United Kingdom in 2018, **** percent of respondents had come across inaccurate news on social media at least once before. Rising concerns over fake news, or information which has been manipulated to influence the public has been a hot topic in recent years. The younger generation however, remains skeptical with nearly **** of Generation Z claiming to be either unconcerned about fake news, or believed that it did not exist altogether.

Percentage data of the number of searches: We investigated the transition of people searching for the keywords "university students", "distance learning" and "online classes" using Google search. (Number of searches) ÷ base value is a relative value, i.e. the data with the highest number of searches in the data is 100%, and the other data are calculated as a percentage as (data) ÷ (most frequent data).

Analysis of ‘How Every NFL Team’s Fans Lean Politically?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nfl-fandome on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 0 samples along with Unnamed: 10, Unnamed: 4, technical information and other features such as: - Unnamed: 3 - Unnamed: 1 - and more.

How to use this dataset

• Analyze Unnamed: 13 in relation to Unnamed: 21
• Study the influence of Unnamed: 7 on Unnamed: 12
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Unweighted Percentages of Weekly Outpatient Visits for ILI and Google Flu Trends dataWe retrieved the weekly unweighted percentages of patient visits due to influenza-like illness (ILI), reported through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet), from http://www.cdc.gov/flu/weekly/ on 10th December 2013. Here, ILI is defined as fever with a temperature of 100°F or greater, accompanied by a cough or a sore throat. Note that the data recorded for a given week can be updated in subsequent weeks, if the CDC have reason to believe that an updated figure would be more accurate. Here, we focus our analysis on the latest data available on the date of retrieval.

We obtained the weekly time series of query volume for searches relating to ILI symptoms from Google Flu Trends (http://www.google.org/flutrends) on 18th December 2013. This time series is restricted to searches made in the United States, and has been shown by Ginsberg et al. to be correlated with the perc...

The Search Engine industry is highly concentrated, with three companies controlling almost the entire industry; the largest company, Alphabet Inc., has a market share greater than 96%. Search engines provide web portals that generate and maintain extensive databases of internet addresses. Industry companies generate most, if not all, of their revenue from advertising. Technological growth has resulted in more households being connected to the Internet, and a boom in e-commerce has made the industry increasingly innovative. Over the past decade, a climb in the percentage of households with internet access has supported revenue growth, while increasing technological integration with daily life has increased demand for industry services. A greater proportion of transactions being carried out online has driven innovation in targeted digital advertising, with declines in rival advertising formats like print media and television increasing the focus on digital marketing as a core strategy. Industry revenue is expected to increase at a compound annual rate of 4.7%, to reach £5.1 billion over the five years through 2024-25. Revenue is forecast to climb by 4.7% in 2024-25. Industry profit has remained high, expanding to 34.2% in 2024-25. The rise of the mobile advertising market and the proliferation of mobile devices mean there are plenty of opportunities for search engines, which are expected to capitalise on these trends further moving forward. Smartphones could disrupt the industry's status quo, as the rising popularity of devices that do not use Google as the default engine benefits other search providers. Technological advancements that incorporate user data are anticipated to make it easier to tailor advertisements and develop new ways of using consumer data. Industry revenue is forecast to jump at a compound annual rate of 6% over the five years through 2029-30, to reach £6.8 billion.

Also shown are the R2 for the statistical model, which linearly regresses the data samples on the daily number of Ebola-related news videos.The percentage of the variance, R2, of the Ebola-related Twitter and Google search samples described by the contagion model of Eq 2 or Eq 3 (as appropriate to the sample); shown are the R2 of the model fit to the full sample, the first half of the sample (model validation training sample), and the extrapolated model prediction for the remaining half of the sample (model validation test sample).

The dataset of this paper is collected based on Google, Blockchain, and the Bitcoin market. Generally, there is a total of 26 features, however, a feature whose correlation rate is lower than 0.3 between the variations of price and the variations of feature has been eliminated. Hence, a total of 21 practical features including Market capitalization, Trade-volume, Transaction-fees USD, Average confirmation time, Difficulty, High price, Low price, Total hash rate, Block-size, Miners-revenue, N-transactions-total, Google searches, Open price, N-payments-per Block, Total circulating Bitcoin, Cost-per-transaction percent, Fees-USD-per transaction, N-unique-addresses, N-transactions-per block, and Output-volume have been selected. In addition to the values of these features, for each feature, a new one is created that includes the difference between the previous day and the day before the previous day as a supportive feature. From the point of view of the number and history of the dataset used, a total of 1275 training data were used in the proposed model to extract patterns of Bitcoin price and they were collected from 12 Nov 2018 to 4 Jun 2021.

This dataset provides processed and normalized/standardized indices for the management tool group 'Supply Chain Management' (SCM), including related concepts like Supply Chain Integration. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding SCM dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "supply chain management" + "supply chain logistics" + "supply chain". Processing: None. The dataset utilizes the original Google Trends index, which is base-100 normalized against the peak search interest for the specified terms and period. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Supply Chain Management + Supply Chain Integration + Supply Chain. Processing: The annual relative frequency series was normalized by setting the year with the maximum value to 100 and scaling all other values (years) proportionally. Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching SCM-related keywords [("supply chain management" OR ...) AND ("management" OR ...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly publication counts in Crossref. Data deduplicated via DOIs. Processing: For each month, the relative share of SCM-related publications (SCM Count / Total Crossref Count for that month) was calculated. This monthly relative share series was then normalized by setting the month with the maximum relative share to 100 and scaling all other months proportionally. Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Supply Chain Integration (1999, 2000, 2002); Supply Chain Management (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Supply Chain Integration" and "Supply Chain Management" were treated as a single conceptual series for SCM. Normalization: The combined series of original usability percentages was normalized relative to its own highest observed historical value across all included years (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Supply Chain Integration (1999, 2000, 2002); Supply Chain Management (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Supply Chain Integration" and "Supply Chain Management" were treated as a single conceptual series for SCM. Standardization (Z-scores): Original scores (X) were standardized using Z = (X - ?) / ?, with ?=3.0 and ??0.891609. Index Scale Transformation: Z-scores were transformed via Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding SCM dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Data underlying: Miller, S., Preis, T., Mizzi, G., Bastos, L. S., Gomes, M. F. d. C., Coelho, F. C., Codeço, C. T., & Moat, H. S. (2022). Faster indicators of chikungunya incidence using Google searches. PLOS Neglected Tropical Diseases, 16, e0010441. doi:10.1371/journal.pntd.0010441.

MillerEtAl_ChikungunyaCaseCountData.csv This file contains data on weekly chikungunya case counts in the city of Rio de Janeiro, aggregated by the week in which the case was first diagnosed (the notification week) and the delay in number of weeks in entering the case in the surveillance system.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notification_week: the epidemiological week in which cases were notified delay_in_weeks: the delay in number of weeks in entering the cases in the surveillance system case_count: the number of cases that were notified in the specified week with the specified delay in number of weeks

MillerEtAl_Fig1A.csv The data underlying Fig. 1A.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 26 May 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 26 May 2019

MillerEtAl_Fig1B.csv The data underlying Fig. 1B.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 21 July 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 21 July 2019

MillerEtAl_Fig1C.csv The data underlying Fig. 1C.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 15 September 2019 notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 15 September 2019

MillerEtAl_Fig2A.csv The data underlying Fig. 2A.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the same week

MillerEtAl_Fig3FigS1A.csv The data underlying Fig. 3 in the main text and Fig. A in S1 Appendix.

notification_week_commencing: the start date of the epidemiological week in which cases were notified notification_week: the epidemiological week in which cases were notified notified_cases: the number of cases notified in the specified epidemiological week baseline_mean: the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week baseline_2.5: the lower bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week baseline_97.5: the upper bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week baseline_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the baseline nowcasting model's 95% prediction interval baseline_error: the difference between the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week and the true number of notified cases baseline_interval_width: the size of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week google_mean: the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_2.5: the lower bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_97.5: the upper bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches google_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the 95% prediction interval produced by the nowcasting model using Google searches google_error: the difference between the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches and the true number of notified cases google_interval_width: the size of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches heuristic: the heuristic model's estimate of the number of cases notified in the specified epidemiological week

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

Historical ILI: Uncertainty quantification of ARLR method’s nowcast (one-week ahead forecast) using historical (without backfill) ILI data for 3 different forecast weeks.

USA: Realtime ILI incidence forecast error comparison.

According to data collected by Pi Datametrics, Google searches for Toyota in the United States increased by 48.3 percent year-over year between the year ending in April 2022 and the year ending in April 2023. Between May 2022 and April 2023, Toyota was the most searched for car brand on U.S. Google.

Mexico: Realtime dengue incidence forecast error comparison.

In April 2025, Google accounted for ***** percent of the search market in the United States across all devices. Bing followed as the second leading search provider in the United States during the last examined month, with a share of around *** percent, among the engine's highest quotas registered in the country to date.

Dataset showing low birth weight births as a percent of all live births.

During the first half of 2024, YouTube was Google's most affected platform by global government content removal requests, with 55.4 percent of removal requests citing the source. Web Search accounted for 32.8 percent of government-related content removal requests.

According to data from Pi Datametrics, Google UK searches for vacations in Spain increased by roughly 2.3 percent in April 2023 over the same month of the previous year. By contrast, Google UK searches for holidays in the United States and Canada declined by around four percent over the same period.