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.

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.

Google Trends data on searches for "pollen" for DMA regions near National Allergy Bureau pollen counting stations from 2012-2017, downloaded in 10x replicates, from Jan-Jun and Apr-Dec of each year. Search data for the term "ragweed" is included as a comparator in pollen searches (no file suffix), and can also be found as a separate search term (in files with the suffix "ragweed.csv")

About this Dataset

Results of scraping Google News search results for "JPY" (2017-2022).

Category

Markets

Keywords

jpy,news,google news,google

Row Count

1233

Price

$1700.00

News and articles that mention "Google". Crawled date: Oct, 2024. Documents count: 12,000.

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.

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} }

Sri Lanka Google Search Trends: Travel & Accommodations: Emirates data was reported at 32.000 Score in 15 May 2025. This records a decrease from the previous number of 33.000 Score for 14 May 2025. Sri Lanka Google Search Trends: Travel & Accommodations: Emirates data is updated daily, averaging 23.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 85.000 Score in 18 Apr 2024 and a record low of 0.000 Score in 19 Nov 2024. Sri Lanka 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 Sri Lanka – Table LK.Google.GT: Google Search Trends: by Categories.

BackgroundOver 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission.Methodology/Principal FindingsWe combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015–2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions.SignificanceGiven the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak.

Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".

Abstract of the article:

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

In February 2025, Microsoft Sites handled **** percent of all search queries in the United States. During the same period, Verizon Media (formerly known as Yahoo and Oath) had a search market share of little less than ** percent. Market leader Google generated **** percent of all core search queries in the United States.

As of June 2024, global searches for the keyword "generative AI" had experienced an increase in the previous year. The search terms for generative artificial intelligence surged in popularity from mid-February to early March 2024, hitting a score of 100 index points in the week ending March 3. Interest in "generative AI" frequently coincides with searches for ChatGPT, an AI chatbot model developed by the United States-based research company OpenAI.

Taiwan: Realtime dengue incidence forecast error comparison.

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).

USA: Realtime ILI incidence forecast error comparison.

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

In March 2024, search platform Google.com generated approximately 85.5 billion visits, down from 87 billion platform visits in October 2023. Google is a global search platform and one of the biggest online companies worldwide.

This data comprises data traces related to search queries used in climate obstruction. It is based on "klimatsans" (Climate Sense or Climate Reason; translated from Swedish, cf. Vowles & Hultman, 2021), a Swedish blog and network which exists since 2014 and runs a Swedish-language blog and submits opinion pieces and letters to the editor to various Swedish news outlets. The stated aims of the network amount to first-level obstruction, i.e. they reject the scientific consensus that increased atmospheric CO2 leads to climate change.

The data concerns how the network throughout its various publications invite readers to “google” certain words (keyphrases). The data set includes: 1) all blog posts published on klimatsans.com from January 2014 to June 2022; 2) all hyperlinks from the blog; 3) tabulation, count, and coding of all search queries suggested in the blog, as identified by following after the Swedish imperative verb "googla"; 4) tabulation of all uses of 25 selected keyphrases in Swedish newspapers; 5) results of search engine results pages for these 25 queries from Google and DuckDuckGo (each run three times: in plain, in verbatim using quotation marks, and preceded by the term "googla") (original data available via Sünkler et al., 2023); 6) tabulation and coding of domains frequently targeted by hyperlinks and/or listed in search engine results pages.

Furthermore, the data set includes some scripts for replication, an extensive README file for methodological additions, and details on coding schemes.

The data was originally collected to investigate to trace data voids through the texts of their creators or proponents. This provides insights into how data voids are created, promoted, used, and if they do not disappear also abandoned.

Biological invasions are a major human induced global change that is threatening global biodiversity by homogenizing the world’s fauna and flora. Species spread because humans have moved species across geographic boundaries and have changed ecological factors that structure ecosystems, such as nitrogen deposition, disturbance, etc. Many biological invasions are caused accidentally, as a byproduct of human travel and commerce driven product shipping. However, humans also have spread many species intentionally because of perceived benefits. Of interest is the role of the recent exponential growth in information exchange via internet social media in driving biological invasions. To date, this has not been examined. Here we show that for one such invasive species, goldenrod, social networks spread misleading and incomplete information that is enhancing the spread of goldenrod invasions into new environments. We show that the notion of goldenrod honey as a “superfood” with unsupported healing properties is driving a demand that leads beekeepers to produce goldenrod honey. Social networks provide a forum for such information exchange and this is leading to further spread of goldenrod in many countries where goldenrod is not native, such as Poland. However, this informal social information exchange ignores laws that focus on preventing the further spread of invasive species and the strong negative effects that goldenrod has on native ecosystems, including floral resources that negatively impact honeybee performance. Thus, scientifically unsupported information on “superfoods” such as goldenrod honey that is disseminated through social internet networks has real world consequences such as increased goldenrod invasions into novel geographical regions which decreases native biodiversity.

Methods Global presence of invasive goldenrod

Data on goldenrod invasiveness (file: Global_data_Goldenrod_status_and planting.xlsx) were collected from literature cited in the Center for Agriculture and Bioscience International website (https://www.cabi.org/isc/datasheet/50599) and our extensive search of literature on goldenrods. We illustrated the results on a map using QGIS open source software.

Global interest in goldenrod plants for apiculture We used Google Search to collect data on the number of internet records containing information about honey produced from goldenrod nectar in countries where the plant is not native, and added these results to the map (file: "Global_data_Goldenrod_status_and planting.xlsx, Fig. 1 in the manuscript). The Google Search tool allowed us to estimate the internet supply of information about this topic (file: International_interest_in_goldenrod.xlsx). The key phrase was ‘goldenrod honey’ and the language was adjusted for each region search; for example, for the UK we used English, for Germany German, for Hungary Hungarian, etc. The words were checked by native speakers or translated using the Google Translate tool, and the relevance of all translations was validated using Google Graphics. In addition, we checked all search results from the first five pages (50 items in total) in the Google Browser to locate all relevant records. We also checked the occurrence of the phrase ‘how to plant goldenrod for bees’ using the Google Search browser. The sentence is more complicated to translate and validate than just ‘goldenrod honey’. Thus, we used only English, German, Russian, and similar languages in relevant countries such as Ukraine and Belarus, and Hungarian, Romanian, and Polish, as native speakers. We checked the first 50 pages (if available) to find all relevant listings. For Europe, we excluded all results about native goldenrods, which are not used in apiculture because they occur at much lower abundances than introduced alien goldenrod species. There are no native goldenrod species in Africa, Australia, or Oceania. All internet searches were performed on 15–17 October 2017.

Global availability of goldenrods in ecommerce in invaded countries

   We checked the availability and purchase of goldenrod seeds, seedlings, and roots on international and local internet platforms in countries where goldenrod has an ‘invasive’ status according to the Centre for Agriculture and Bioscience International (file: International_Goldenrod_ecommerce.xlsx). We used the following platforms: Amazon, eBay, Etsy, Allegro, and OLX. Search was performed on 27 May 2020.

Global availability of information about goldenrod honey as a superfood and its healing properties

We checked the abundance of information on the internet about the healing properties of goldenrod honey using the Google Search browser and various phrases about healing different ailments (file: Goldenrod_honey_properties.xlsx). Search performed on 26 May 2020.

Local interest in goldenrod plants for apiculture

    To increase data precision and completeness, we focussed on one specific country, Poland, where we used data from the biggest internet platforms, Allegro (www.allegro.pl) and OLX (www.olx.pl). We searched both platforms for goldenrod honey availability, price, and purchase from 2008 to 2018 (file: Ecommerce_goldenrod_honey_Poland.xlsx). We also used internet tools such as Google Trends and the Google Search browser to closely examine beekeeper and customer interest in goldenrods (file: Google_Trends_goldenrod_honey_Poland.xlsx). Google Trends (https://trends.google.com/trends/) is a public web facility provided by Google Inc. that measures how often a particular search item is entered into Google Search browsers, relative to the total search volume. The trends provided by this tool estimate changes in searches for an item or phrase and are often used to examine temporal changes in socio-economic studies. To avoid biases, we set the ‘food’ filter, allowing searches to only find food-related items when searching for goldenrod honey, thereby avoiding searches for gardening plants. For Poland, we checked the Google Search browser for trends in available information on goldenrod honey as a healing superfood (file: Goldenrod_honey_properties.xlsx). We checked these trends for general healing properties and for kidney problems as an example. Using the Google Search browser, we also checked internet forums where beekeepers exchanging advice and knowledge, to check where they plant goldenrod for bees and if they have noted deteriorating influences on their honeybee colonies (file: Advices_where_to_plant_goldenrod_Poland.xlsx). Specifically, in forums for beekeepers, we searched for the number of posts per year and the number of people offering advice about planting goldenrod for bees. We noted the typical habitat most often recommended for planting this species. The forums were active from 2008 to 2018.

Avoiding bias in collecting sociological data from the internet

To collect sociological data on the interest of beekeepers in goldenrod, we used triangulation as the best advised method for collecting non-experimental data in social science. The method suggests using at least three different sources of data or to address at least three different questions about each researched topic. For this reason, we conducted our study globally and locally, using Google Search, Google Trends, and ecommerce portals, with several research questions. Our research was conducted according to the methods advised by Google Inc. to deal with the inaccuracy of Google Search results. We followed the official instructions provided by Google to improve the results. We used two filters officially advised by Google IT. The first was the “rc = 1” request parameter to request an accurate result count for up to 1 M documents (source of the advice: https://support.google.com/gsa/answer/2672285?hl=en). The second was r = 0, allowing for directory filtering (filtering documents coming from the same folder) and duplicate sniper filtering (if two documents have the same generated snippet, they will be filtered). Both filters were officially advised by Google: https://stackoverflow.com/questions/33426045/how-to-get-an-accurate-m-value-from-the-google-search-appliance-api-with-php, and checked by programmers from the University of Queensland. However, neither of these two filters changed the results.

Seasonal changes in native food availability for honeybees

We used data from field surveys conducted in 2014 and 2015 to examine the abundance dynamics of flowering species in grasslands invaded by goldenrod and in two types of control grassland (file: Bee_abundance_and_resource_dynamics.xlsx). We chose 10 grasslands invaded by goldenrods, 10 control grasslands that had recently been set aside, and 10 control grasslands that were managed (cut twice a year in June/July and late August). In each grassland, five 16 m2 quadrants were randomly placed. Plant species richness and flower abundance were estimated within these quadrants. Plant species were counted every two weeks starting in early April and ending at the beginning of November in both 2014 and 2015. During each survey, the flower abundance of each plant species was estimated as a categorical variable: 0, 1–10, 11–50, 51–100, 101–150, 151–200, 201–250, 251–300, 301–350, 351–400, and >400 flowers/inflorescences per 16 m2. We compared flowering species dynamics from early spring to late summer in the goldenrod invaded grasslands and the abandoned and mown control grasslands within these quadrants. We also counted all honeybees that foraged in each plot during each plant survey.

Experiment on the lifespan of bees fed on goldenrod honey

We experimentally measured the lifespans of honeybee workers fed on fresh goldenrod nectar (honey) and compared them with two control groups: workers fed on fresh honey from a mixture of native flowers and workers fed on sugar dissolved in water, which is typically

In the last week of January 2024, global Google searches for the wording "Taylor Swift AI" skyrocketed. This was due to the circulation of artificial intelligence-generated sexually explicit images of the singer and perform. The use of AI to create non-consensual deepfake explicit material has been impacting celebrities and normal users alike, with destructive effects on the mental health of affected individuals. Generative AI and so-called synthetic media have been used in image-based abuse, as well as in child sexual abuse material (CSAM).