In 2023, "weather" was the most searched keyword on Google in the United States, with almost 82.62 million online searches monthly. "youtube" ranked second with an average of 71.3 million searches per month. The video platform generated nearly 582.7 million searches annually, of which over 99 percent were organic. The keyword for "amazon" came in third with over 56 million monthly searches, while "facebook" was the fourth-most popular keyword searched on Google, with over 54 million monthly searches.

In 2024, "YouTube" was the most searched keyword on Google, with over 346 million monthly online searches. In addition, over 99 percent of its traffic was organic. The second most popular keyword was "facebook", with 228 million monthly searches. "Amazon" ranked third with approximately 163.94 million searches monthly. Additionally, the keyword "translate" ranked fourth, with around 141.5 million searches monthly.

In 2023, 'weather' was the most searched keyword on Google in the United Kingdom, with over 18.3 million monthly searches on average. The news website 'bbc news' came in second with more than 17.1 million monthly searches. The video platform 'youtube' did not fall behind, generating around 16.1 million searches per month, while also receiving nearly 117 million searches annually, of which over 99 percent were organic. Additionally, "facebook" was the fourth-most popular keyword searched on Google, with approximately 16.1 million searches monthly.

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.

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.

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.

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

In 2023, the most common terms that Hong Kong internet users entered into Google search were "Hong Kong (in Chinese)", Google", "translate", and "YouTube". The word "weather" in Chinese received 25 percent of the search volume of the top query during 2023.

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.

According to data from Pi Datametrics, the most searched term on Google from January to April 2020 in the United Kingdom (UK) was "airpods".

"Tiempo", "Clima", and "Tiempo mañana" were some of the most searched queries in the Google search engine in Mexico in 2023, indexing at 100, 55, and 23 in terms of search volume, respectively. Compared to the top-ranking search result ("Tiempo"), "Traductor", "Ingles Español", "Traductor ingles", and "Traductor Ingles Español" scored relative index values of 82, 17, 15, and 14. Social networks like WhatsApp, Facebook and YouTube, also appeared with a high frequency throughout the chart, followed by topics related to online video consumption.

Dataset

This dataset was created by Gamze Akkurt

Contents

"Tiempo" and "El tiempo" were the most searched queries in the Google search engine in Spain in 2023, with 100 and 40 index ratings respectively. Within an index of 100, "Traductor" scored third with a rating of 39, followed by "Barcelona", with 30.

Research datasets about top signals for covid 19 (coronavirus) for study into Google Trends (GT) and with SEO metrics

Website

The study is currently published on https://covidgilance.org website (in french)

Datasets description

covid signals -> |selection| -> 4 dataset -> |serp.py| -> 4 serp datasets -> |aggregate_serp.pl| -> 4 aggregated dataset of serp -> |prepare datasets| -> 4 ranked top seo dataset

Original lists of signals (mainly covid symptoms) - dataset

Description: contain the original relevant list of signals for covid19 (here list of queries where you can see, in GT, a relevant signal during the covid 19 period of time)
Name: covid_signal_list.tsv

List of content:

- id: unique id for the topic
- topic-fr: name of the topic in French
- topic-en: name of the topic in English
- topic-id: GT topic id
- keyword fr: one or several keywords in French for GT
- keyword en: one or several keywords in English for GT
- fr-topic-url-12M: link to 12-months French query topic in GT in France
- en-topic-url-12M: link to 12-months English query topic in GT in US
- fr-url-12M: link to 12-months French queries in GT in France
- en-url-12M: link to 12-months English queries topic in GT in US
- fr-topic-url-5M: link to 5-months French query topic in GT in France
- en-topic-url-5M: link to 5-months English query topic in GT in US
- fr-url-5M: link to 5-months French queries in GT in France
- en-url-5M: link to 5-months English queries topic in GT in US

Tool to get SERP of covid signals - tool

Description: query google with a list of covid signals and obtain a list of serps in csv (tsv in fact) file format
Name: serper.py

python serper.py

SERP files - datasets

Description Serp results for 4 datesets of queries Names: simple version of covid signals from google.ch in French: serp_signals_20_ch_fr.csv
simple version of covid signals from google.com in English: serp_signals_20_en.csv
amplified version of covid signals from google.ch in French: serp_signals_covid_20_ch_fr.csv
amplified version of covid signals from google.com in English: serp_signals_covid_20_en.csv

amplified version means that for each query we create two queries one with the keywords "covid" and one with "coronavirus"

Tool to aggregate SERP results - tool

Description: load csv serp data and aggregate the data to create a new csv file where each line is a website and each column is a query. Name: aggregate_serp.pl

`perl aggregate_serp.pl> aggregated_signals_20_en.csv

datasets of top website from the SERP results - dataset

Description a aggregated version of the SERP where each line is a website and each column a query
Names:
aggregated_signals_20_ch_fr.csv
aggregated_signals_20_en.csv
aggregated_signals_covid_20_ch_fr.csv
aggregated_signals_covid_20_en.csv

List of content:

- domain: domain name of the website
- signal 1: Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal ...: Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal n: Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- total: average position (total of all position /divided by the number of queries)
- missing: Total number of missing results in the SERP for this website

datasets ranked top seo - dataset

Description a ranked (by weighted average position) version of the aggregated version of the SERP where each line is a website and each column a query. TOP 20 have more information about the type and HONcode validity (from the date of collect: September 2020)

Names:
ranked_signals_20_ch_fr.csv
ranked_signals_20_en.csv
ranked_signals_covid_20_ch_fr.csv
ranked_signals_covid_20_en.csv

List of content:

- domain: domain name of the website
- signal 1: Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal ...: Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal n: Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- avg position: average position (total of all position /divided by the number of queries)
- nb missing: Total number of missing results in the SERP for this website
- % presence: % of presence
- weighted avg postion: combination of avg position and % of presence for final ranking
- honcode: status of the Honcode certificate for this website (none/valid/expired)
- type: type of the website (health, gov, edu or media)

Google Search Trends: Travel & Accommodations: Emirates data was reported at 2.000 Score in 19 Mar 2025. This stayed constant from the previous number of 2.000 Score for 18 Mar 2025. Google Search Trends: Travel & Accommodations: Emirates data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 19 Mar 2025, with 1205 observations. The data reached an all-time high of 43.000 Score in 25 May 2023 and a record low of 0.000 Score in 17 Mar 2025. Google Search Trends: Travel & Accommodations: Emirates data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Computer & Electronics: Samsung Electronics data was reported at 66.000 Score in 19 Mar 2025. This records an increase from the previous number of 58.000 Score for 18 Mar 2025. Google Search Trends: Computer & Electronics: Samsung Electronics data is updated daily, averaging 54.000 Score from Dec 2021 (Median) to 19 Mar 2025, with 1205 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} }

Cambodia Google Search Trends: Computer & Electronics: Apple data was reported at 31.000 Score in 18 Mar 2025. This records a decrease from the previous number of 39.000 Score for 17 Mar 2025. Cambodia Google Search Trends: Computer & Electronics: Apple data is updated daily, averaging 28.000 Score from Dec 2021 (Median) to 18 Mar 2025, with 1204 observations. The data reached an all-time high of 100.000 Score in 10 Sep 2024 and a record low of 0.000 Score in 06 Mar 2023. Cambodia Google Search Trends: Computer & Electronics: Apple data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Cambodia – Table KH.Google.GT: Google Search Trends: by Categories.

In 2024, La Roche Posay ranked as the most searched beauty brand on Google worldwide. Google users searched for the popular French skincare brand approximately 33.6 million times. Another skincare brand placed second, Cerave, with around 33.4 million searches. Sol De Janeiro came in third, accumulating roughly 32 million Google searches.

"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 68 points compared to 100 for the most popular term. Other popular search queries were "Facebook", "YouTube", and "Meteo".