In 2024, "Google" was the most popular search query on Google. "You" ranked second, scoring an index value of 79 points. "YouTube" ranked third with an index value of 76 points relative to the top query, while "Facebook" ranked fifth, with an index value of 62.

In February 2025, Verizon Media processed **** million online search queries in the United States, and market leader Google processed over **** million search queries. In total, Google accounted for ** percent of the U.S. desktop search queries. Google was also the leading mobile search provider in the United States, accounting for around ** percent of the market as of March 2024.

As of January 2020, ** percent of all U.S. online search queries contained *** keywords. Three word search terms accounted for ***** percent of searches. Queries up to ***** words accounted for over ** percent of online searches in the United States.

Between January 1st and December 31st, 2024, "Weather" was the most popular search query on the Google search engine. "Google" was ranked second, reaching an index point value of 93 relative to the top search query.

Google Trends allows you to study the trends and patterns of search queries on Google. Google Trends represents the absolute number of searches relative to the total number of searches in a defined period of interest. The number retrieved ranges from 0 to 100, where 100 is the highest relative search term for a given search query in the period of interest. 2The data was collecte rom January 2018 to January 2022.

With an average of 1.3 billion monthly searches, "youtube" was the most popular online search query in 2024. The social platform Facebook and messenger app WhatsApp were the next most searched queries on Google, 755 million and 562 million. The most popular question on Google search was "what to watch".

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.

This is a curated dataset of Google Trends over the years. Every year, Google releases the trending search queries all over the world in various categories. It has trends from 2001 to 2020.

Image Credits: Unsplash - lukecheeser

The most popular search query on Google in Kazakhstan in 2024 was "Weather" ("Погода"). It was followed by "Translator" ("Переводчик") with 59 percent of the search volume of the leading query. Kundelik (Кунделик), a domestic learning service platform for students and educators, ranked sixth.

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.

We present Qbias, two novel datasets that promote the investigation of bias in online news search as described in

Fabian Haak and Philipp Schaer. 2023. 𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions. In Proceedings of ACM Web Science Conference (WebSci’23). ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3578503.3583628.

Dataset 1: AllSides Balanced News Dataset (allsides_balanced_news_headlines-texts.csv)

The dataset contains 21,747 news articles collected from AllSides balanced news headline roundups in November 2022 as presented in our publication. The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The AllSides balanced news aims to offer multiple political perspectives on important news stories, educate users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles. Overall, the dataset contains 10,273 articles tagged as left, 7,222 as right, and 4,252 as center.

To provide easier access to the most recent and complete version of the dataset for future research, we provide a scraping tool and a regularly updated version of the dataset at https://github.com/irgroup/Qbias. The repository also contains regularly updated more recent versions of the dataset with additional tags (such as the URL to the article). We chose to publish the version used for fine-tuning the models on Zenodo to enable the reproduction of the results of our study.

Dataset 2: Search Query Suggestions (suggestions.csv)

The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total number of topics. The topic tags include names, a wide range of political terms, agendas, and topics (e.g., "communism", "libertarian party", "same-sex marriage"), cultural and religious terms (e.g., "Ramadan", "pope Francis"), locations and other news-relevant terms. On average, the dataset contains 469 search queries for each topic. In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.

The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion" and "rank" represents the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".

We retrieved ten search query suggestions provided by the Google and Bing search autocomplete systems for the input of each of these root queries, without performing a search. Furthermore, we extended the root queries by the letters a to z (e.g., "democrats" (root term) >> "democrats a" (query input) >> "democrats and recession" (query suggestion)) to simulate a user's input during information search and generate a total of up to 270 query suggestions per topic and search engine. The dataset we provide contains columns for root term, query input, and query suggestion for each suggested query. The location from which the search is performed is the location of the Google servers running Colab, in our case Iowa in the United States of America, which is added to the dataset.

AllSides Scraper

At https://github.com/irgroup/Qbias, we provide a scraping tool, that allows for the automatic retrieval of all available articles at the AllSides balanced news headlines.

We want to provide an easy means of retrieving the news and all corresponding information. For many tasks it is relevant to have the most recent documents available. Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information. By providing the scraper we facilitate access to a recent version of the dataset for other researchers.

In 2023, iltalehti was the most widely used query Finns typed into Google search. A great number of people also searched for sää (english: weather), and ilta sanomat.

The Google Trends dataset will provide critical signals that individual users and businesses alike can leverage to make better data-driven decisions. This dataset simplifies the manual interaction with the existing Google Trends UI by automating and exposing anonymized, aggregated, and indexed search data in BigQuery. This dataset includes the Top 25 stories and Top 25 Rising queries from Google Trends. It will be made available as two separate BigQuery tables, with a set of new top terms appended daily. Each set of Top 25 and Top 25 rising expires after 30 days, and will be accompanied by a rolling five-year window of historical data in 210 distinct locations in the United States. This Google dataset is hosted in Google BigQuery as part of Google Cloud's Datasets solution and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

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  Dataset Description

Dataset Name: Google Search Trends Top Rising Search Terms Description: The Google Search Trends Top Rising Search Terms dataset provides valuable insights into the most rapidly growing search queries on the Google search engine. It offers a comprehensive collection of trending search… See the full description on the dataset page: https://huggingface.co/datasets/hoshangc/google_search_terms_training_data.

No description was included in this Dataset collected from the OSF

The International Google Trends dataset will provide critical signals that individual users and businesses alike can leverage to make better data-driven decisions. This dataset simplifies the manual interaction with the existing Google Trends UI by automating and exposing anonymized, aggregated, and indexed search data in BigQuery. This dataset includes the Top 25 stories and Top 25 Rising queries from Google Trends. It will be made available as two separate BigQuery tables, with a set of new top terms appended daily. Each set of Top 25 and Top 25 rising expires after 30 days, and will be accompanied by a rolling five-year window of historical data for each country and region across the globe, where data is available. This Google dataset is hosted in Google BigQuery as part of Google Cloud's Datasets solution and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

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

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

Search engines, which collect, organize and display knowledge of the internet, are the backbone of the information age and have helped popularize the ad-supported attention economy that prevails throughout the internet. From 2019 to 2024, spending on internet advertising has maintained strong momentum as consumer demand for internet access continued to surge, driven by the adoption of LTE, 5G and unlimited mobile data plans. Despite COVID-19 depressing total advertising expenditure, digital advertising continued to grow as consumers practically lived online while stay-at-home orders were in place. As a result, search engine revenue from advertising is slated to mount at a CAGR of 10.4% to $287.5 billion, including an anticipated hike of 8.4% in 2024, with profit at 18.7%. The search engine industry is fundamentally differentiated from the rest of the economy by its advertising sales framework, market aggregation and high interconnection with other industries. While search is a consumer product, search revenue comes from a platform's desirability to advertisers, not users. Search platforms must balance providing the best search experience while integrating as many advertisements as possible. This difficult balance is challenging to achieve because advertising dollars tend to scale best on the leading search platform, increasing aggregation forces for search providers. The market leaders in search, Google and Microsoft, have met this balance by using advertising revenue to grow a suite of services designed to collect extensive behavior information on and off the search website. This data then targets ads to hyper-specific markets, funding the search business model. As the number of hours spent on the internet continues to mount, search engine revenue is poised to climb at a CAGR of 7.1% to $404.9 billion through the end of 2029. Advertisers will rely increasingly on search engine marketing due to its cost-effectiveness and efficiency advantages over traditional media. With proper analytics software installed, marketers can track which terms, advertisements and websites are the most effective, enabling incremental real-time tweaks and improvements in advertising campaigns. Artificial intelligence has promised to change the purpose of search from navigation to finding answers, which will change the structure of the internet, just as search engine providers have done many times before.

[Abstract] Software development is a knowledge-intensive activity. Official documentation for developers may not be sufficient for all developer needs. Searching for information on the Internet is a usual practice, but finding really useful information may be challenging, because the best solutions are not always among the first ranked pages. So, developers have to read and discard irrelevant pages, that is, pages that do not have code examples or that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results that remove irrelevant pages. The approach works as follows: a query related to the programming task is prepared, and given as an input to a search engine. The returned pages pass through an automatic filter to select relevant pages. We evaluated the top-20 pages returned by the Google search engine, for 10 different queries, and observed that only 31\% of the evaluated pages are relevant to developers. Then, we proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google’s search engine has been used as a baseline, and our results have shown that Google’s search engine returns a reasonable number of irrelevant pages for developers, and we could find an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customized web search filter for development content. [Contents of Research Data.rar file] The Research Data.rar file has a folder called Research Data that contains 3 folders internally, with the names: “01 – Source Code”, “02 - Data” and “03 – Preprocessing rules”. The folder “01 – Source Code” contains the JAVA source code of the implementations of the proposed approaches. The folder “02 - Data” contains the data of the evaluations carried out in the work, which are in the folders “01 - Evaluation results of pages returned by Google” and “02 - Results of approaches comparisons”. The folder “01 - Evaluation results of pages returned by Google” has the evaluations carried out on the first 20 pages returned by Google, following the criteria defined in the work, for the 10 queries considered in the evaluation. The folder “02 - Results of approaches comparisons” contains the results of the evaluation of the proposed approaches, for the 10 queries considered in the evaluation. In this evaluation, the number of pages given as input for the approaches was increased from 3 to 20 pages, for each number of pages a folder was generated with the results. In addition to the results of the Precision, Recall and F-Measure metrics that are in the file named Results Approaches.txt, other files were generated for analysis. For example, the Instances_without_outliers.txt file shows which pages were filtered out after applying the outlier page removal filter. The Selected Pages Approach 4.txt file, on the other hand, shows which pages were filtered after applying the filters of the GORCUO approach. The folder “03 - Preprocessing rules” has a file called Rules.java. In this file, there is the commented JAVA source code, from the implementation of the rules created in the pre-processing stage of the proposed approach.

The most popular search query on Google in Czechia in 2024 was "Seznam". It was followed by "Počasí" with ** percent of the search volume of the leading query.