"*******" 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.

'*******' 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/

"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.

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.

Keywords: Border Embassy Disconnected Cyber security Data protection Malware Hack Cyber attack VPN

Time period covered: January 1 2022 to March 31st 2022

Dataset

This dataset was created by margual56

Contents

The dataset contains:

1) The list of tweets corresponding to the keywords "heat wave india" and "heatwave india" between 2010 and 2017.

2) The daily count of the same tweets

3) The monthly Google Trends data corresponding to the keywords "heat wave", "heatwave", "heat wave india", and "heatwave india" limited to the searches from India in the period 2010-2017

The Twitter data has been obtained wth the Python package Get-Old-Tweets (https://github.com/Jefferson-Henrique/GetOldTweets-python); the Google Trends data are obtained from the Google Trends webpage (https://trends.google.com/trends/?geo=US).

Google Search Trends: Travel & Accommodations: Lufthansa data was reported at 0.000 Score in 14 May 2025. This records a decrease from the previous number of 1.000 Score for 13 May 2025. Google Search Trends: Travel & Accommodations: Lufthansa data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 22.000 Score in 16 Oct 2022 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Travel & Accommodations: Lufthansa 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.

Results of seasonal Mann-Kendall trend test on the search volume in Google for different topics using different benchmark keywords.

Minute-by-minute updated keyword database from Google, featuring 230 trending search terms

A dataset of the most effective gaming-related SEO keywords based on YouTube and Google trends.

As of February 2022, the most popular call-to-action (CTA) and keyword present in the description of the leading health and fitness apps in the Google Play Store was "help you," with around *** mentions. The keyword "weight loss" followed with *** mentions, while "track your" and "heart rate" ranked third and fourth, with *** and *** mentions, respectively.

Minute-by-minute updated keyword database from Google, featuring 509 trending search terms

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

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)

The market for trend tracking tools is experiencing robust growth, projected to reach $1312 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.2% from 2025 to 2033. This expansion is driven by several key factors. The increasing reliance on data-driven decision-making across various sectors, including marketing, product development, and business intelligence, fuels the demand for sophisticated trend analysis tools. Furthermore, the proliferation of online data sources, coupled with the complexity of identifying meaningful trends within this vast landscape, creates a significant need for tools that can efficiently filter, analyze, and visualize trends. The competitive landscape is populated by a diverse range of players, from established market leaders like Semrush and BuzzSumo to innovative startups such as Exploding Topics and SparkToro. These companies offer a spectrum of functionalities, catering to different user needs and budgets, ranging from basic keyword research to comprehensive social listening and competitive analysis capabilities. The continued evolution of artificial intelligence (AI) and machine learning (ML) within these tools is further accelerating market growth, enabling more accurate trend prediction and more efficient data processing. This growth is expected to continue, driven by factors such as increasing marketing budgets allocated to digital channels, heightened competition requiring proactive trend identification, and the ever-growing volume of online data generating new opportunities for trend discovery. However, challenges remain. The market faces increasing competition, requiring continuous innovation and differentiation to maintain market share. Furthermore, the integration of these tools into existing workflows can sometimes prove challenging for businesses, potentially hindering adoption. Despite these obstacles, the long-term outlook for trend tracking tools remains positive, fueled by the continuous need for organizations to stay ahead of the curve and make informed decisions in a rapidly evolving digital environment.

Minute-by-minute updated keyword database from Google, featuring 304 trending search terms

Minute-by-minute updated keyword database from Google, featuring 403 trending search terms