"*******" was the most frequently searched keyword on Google worldwide, with over ***** million monthly online searches during the analyzed period of January to March in 2025. Furthermore, the search resulted in more than ***** million website visits, or more than **** percent of all traffic. With *** million monthly searches, "***" was the second most popular keyword, and "***********" came in third place with about ****** million searches per month.

'*******' was the most searched keyword on Google in the United States between January and March 2025, with an average monthly volume of ****** million searches over the researched period. "***" ranked second with a search volume of ***** million searches. The keyword for "nfl" came in third, making up to ***** million searches.

"Squid Game" was the keyword that had the highest maximum monthly search volume in 2021, with 101 million online searches worldwide in its peak month. The second most trending keyword based on its peak search volume was "Christian Eriksen." Eriksen is a Danish football player who experienced cardiac arrest during the Euro 2020 Denmark against Finland match, on June 12, 2021. Another popular keyword search on Google was Queen Elisabeth's late husband "Prince Phillip," with a peak of 37.2 million searches. Another popular topic related to the British royal family was Prince Harry and Meghan Markle's interview with Oprah Winfrey in March 2021. This was the first big interview after the couple decided to step back as senior royals, and queries on the topic went up to 1.2 million searches on Google in 2021.

You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

'*******' was the most searched keyword on Google in the United Kingdom between January and March 2025, with an average monthly volume of ***** million searches over the researched period. Over **** million website visits were generated by this keyword, which accounted for **** percent of the country's searches. '**************' ranked second, with ** million searches and **** million website visits per month during the same time period, followed by numerous queries related to football and the championship. Additionally, "*******" was the tenth most frequently searched keyword on Google in the United Kingdom.**********************************************************************************************************************************************************************

Abstract (our paper) The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends. Data personal-name.txt.gz: The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014. personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz: The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view. Publication This data set was created for our study. If you make use of this data set, please cite: Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015. http://dx.doi.org/10.1145/2786451.2786495 http://arxiv.org/abs/1509.02218 (author-created version) Note The raw data of Wikipedia page views is available in the following page. http://dumps.wikimedia.org/other/pagecounts-raw/ {"references": ["Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015.", "Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Analysis for Search Trend Prediction. Proceedings of the Annual Conference of Japanese Society for Artificial Intelligence (in Japanese). vol.29, no.2I1-1, pp.1-4, 2015."]}

A dataset of fashion keywords, including their definitions, synonyms, antonyms, search volume and costs.

The most searched keywords on Google Shopping by French Internet users throughout the year 2020 are represented in this statistic. That year, Nike was the top query searched on Google Shopping, followed by the keyword "Amazon" with an index of 84, meaning that this last query received 84 percent of the search volume of "Nike". "Samsung" came in third with and index of 57 and "Chaussure" (meaning shoe) occupied the fourth spot with an index of 43 out of 100.

In 2023, 'Weather' in traditional Chinese was the most searched query on Google in Taiwan. The term 'PTT', a bulletin board system (BBS), and 'translate' in traditional Chinese were other popular keywords, receiving 91 percent and 73 percent of the search volume of 'Weather' during 2023 respectively.

This represents the top 10 searches that visitors have conducted on via Google Search. The data represents the most recent one-month period. *Note: On July 1, 2023, standard Universal Analytics properties will stop processing data.

Minute-by-minute updated keyword database from Google, featuring 477 trending search terms

These are the top trending "How to" searches on Google, ranked by their spike value. Trending searches are searches with the biggest increase in search interest since the previous time period. Data covers the past 5 years.

"Traduction" and "Google" were the most popular Google search queries in Tunisia in 2023. The following leading online search on Google was "Google Traduction", which obtained an index of ** points compared to 100 for the most popular term. Other popular search queries were "Facebook", "YouTube", and "Meteo".

Minute-by-minute updated keyword database from Google, featuring 3 trending search terms

A dataset of web design keywords, including their definitions, synonyms, antonyms, search volume and costs.

"Meteo", "Google", and "Traduction" were the most entered queries in the Google search engine in France in 2024, with index ratings of ***, **, and **, respectively. These were followed by "Météo" and "YouTube", scoring ** and ** index points relative to the most searched query.

In 2022, Wordle was the most popular Google search in the United States, the United Kingdom, Canada, and Spain. By contrast, in Germany and Italy, Ukraine was the most popular search on Google. In Brazil, Google users were interested the most in the 2022 elections.

Wordle!

Launched in October 2021 and purchased by the New York Times in January 2022, Wordle is an online game where players can play only once a day and have to guess a five-letter word in six tries. The game became an instant hit, with a surge of daily users at the beginning of the year. For instance, it was one of the most popular iPhone apps in the United States, being played mostly by millennials. Also in the United Kingdom, the game gained in popularity, with most players using the app every day.

Google search

As the leading search engine in many countries, Google is the most visited multi-platform web property. Indeed, most of Google's revenues come from Google properties, which include the search platform, the traffic generated by search distribution partners using Google.com as their default search in browsers, and the advertising on its own sites.

Google Search Trends: Computer & Electronics: Samsung Electronics data was reported at 59.000 Score in 15 May 2025. This records a decrease from the previous number of 67.000 Score for 14 May 2025. Google Search Trends: Computer & Electronics: Samsung Electronics data is updated daily, averaging 55.500 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 100.000 Score in 23 Dec 2023 and a record low of 0.000 Score in 02 Jul 2023. Google Search Trends: Computer & Electronics: Samsung Electronics data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Sri Lanka – Table LK.Google.GT: Google Search Trends: by Categories.

This dataset contains a collection of around 2,000 HTML pages: these web pages contain the search results obtained in return to queries for different products, searched by a set of synthetic users surfing Google Shopping (US version) from different locations, in July, 2016.

Each file in the collection has a name where there is indicated the location from where the search has been done, the userID, and the searched product: no_email_LOCATION_USERID.PRODUCT.shopping_testing.#.html

The locations are Philippines (PHI), United States (US), India (IN). The userIDs: 26 to 30 for users searching from Philippines, 1 to 5 from US, 11 to 15 from India.

Products have been choice following 130 keywords (e.g., MP3 player, MP4 Watch, Personal organizer, Television, etc.).

In the following, we describe how the search results have been collected.

Each user has a fresh profile. The creation of a new profile corresponds to launch a new, isolated, web browser client instance and open the Google Shopping US web page.

To mimic real users, the synthetic users can browse, scroll pages, stay on a page, and click on links.

A fully-fledged web browser is used to get the correct desktop version of the website under investigation. This is because websites could be designed to behave according to user agents, as witnessed by the differences between the mobile and desktop versions of the same website.

The prices are the retail ones displayed by Google Shopping in US dollars (thus, excluding shipping fees).

Several frameworks have been proposed for interacting with web browsers and analysing results from search engines. This research adopts OpenWPM. OpenWPM is automatised with Selenium to efficiently create and manage different users with isolated Firefox and Chrome client instances, each of them with their own associated cookies.

The experiments run, on average, 24 hours. In each of them, the software runs on our local server, but the browser's traffic is redirected to the designated remote servers (i.e., to India), via tunneling in SOCKS proxies. This way, all commands are simultaneously distributed over all proxies. The experiments adopt the Mozilla Firefox browser (version 45.0) for the web browsing tasks and run under Ubuntu 14.04. Also, for each query, we consider the first page of results, counting 40 products. Among them, the focus of the experiments is mostly on the top 10 and top 3 results.

Due to connection errors, one of the Philippine profiles have no associated results. Also, for Philippines, a few keywords did not lead to any results: videocassette recorders, totes, umbrellas. Similarly, for US, no results were for totes and umbrellas.

The search results have been analyzed in order to check if there were evidence of price steering, based on users' location.

One term of usage applies:

In any research product whose findings are based on this dataset, please cite

@inproceedings{DBLP:conf/ircdl/CozzaHPN19, author = {Vittoria Cozza and Van Tien Hoang and Marinella Petrocchi and Rocco {De Nicola}}, title = {Transparency in Keyword Faceted Search: An Investigation on Google Shopping}, booktitle = {Digital Libraries: Supporting Open Science - 15th Italian Research Conference on Digital Libraries, {IRCDL} 2019, Pisa, Italy, January 31 - February 1, 2019, Proceedings}, pages = {29--43}, year = {2019}, crossref = {DBLP:conf/ircdl/2019}, url = {https://doi.org/10.1007/978-3-030-11226-4_3}, doi = {10.1007/978-3-030-11226-4_3}, timestamp = {Fri, 18 Jan 2019 23:22:50 +0100}, biburl = {https://dblp.org/rec/bib/conf/ircdl/CozzaHPN19}, bibsource = {dblp computer science bibliography, https://dblp.org} }