A study released in June 2025 that looked at about 82,000 websites found that Google was responsible for almost ** percent of the traffic generated to these domains. Direct traffic corresponded to around **** percent of the investigated websites' traffic volume. While traditional search engines like Bing and social networks like Facebook represented larger shares, ChatGPT overtook Reddit and LinkedIn with a slightly larger share, indicating an increase in traffic from these platforms.

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.

Web design service companies have experienced significant growth over the past few years, driven by the expanding use of the Internet. As online operations have become more widespread, businesses and consumers have increasingly recognized the importance of maintaining an online presence, leading to robust demand for web design services and boosting the industry’s profit. The rise in broadband connections and online business activities further spotlight this trend, making web design a vital component of modern commerce and communication. This solid foundation suggests the industry has been thriving despite facing some economic turbulence related to global events and shifting financial climates. Over the past few years, web design companies have navigated a dynamic landscape marked by both opportunities and challenges. Strong economic conditions have typically favored the industry, with rising disposable incomes and low unemployment rates encouraging both consumers and businesses to invest in professional web design. Despite this, the sector also faced hurdles such as high inflation, which made cost increases necessary and pushed some customers towards cheaper substitutes such as website templates and in-house production, causing a slump in revenue in 2022. Despite these obstacles, the industry has demonstrated resilience against rising interest rates and economic uncertainties by focusing on enhancing user experience and accessibility. Overall, revenue for web design service companies is anticipated to rise at a CAGR of 2.2% during the current period, reaching $43.5 billion in 2024. This includes a 2.2% jump in revenue in that year. Looking ahead, web design companies will continue to do well, as the strong performance of the US economy will likely support ongoing demand for web design services, bolstered by higher consumer spending and increased corporate profit. On top of this, government investment, especially at the state and local levels, will provide further revenue streams as public agencies seek to upgrade their web presence. Innovation remains key, with a particular emphasis on designing for mobile devices as more activities shift to on-the-go platforms. Companies that can effectively adapt to these trends and invest in new technologies will likely capture a significant market share, fostering an environment where entry remains feasible yet competitive. Overall, revenue for web design service providers is forecast to swell at a CAGR of 1.9% during the outlook period, reaching $47.7 billion in 2029.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

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}
}

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The goal of this research is to examine direct answers in Google web search engine. Dataset was collected using Senuto (https://www.senuto.com/). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.

Dataset contains the following elements:

keyword,

number of monthly searches,

featured domain,

featured main domain,

featured position,

featured type,

featured url,

content,

content length.

Dataset with visibility structure has 743 798 keywords that were resulting in SERPs with direct answer.

In July 2023, the majority of browser web traffic in the Benelux region was generated via mobile phones. However, laptop and desktop devices accounted for over 46 percent of web traffic in the Netherlands. In Belgium, laptops and desktops accounted for approximately 38 percent of web traffic, and similar values were observed in Luxembourg as well.

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

Peru Internet Usage: Search Engine Market Share: All Platforms: Seznam data was reported at 0.040 % in 28 Nov 2024. This records an increase from the previous number of 0.000 % for 27 Nov 2024. Peru Internet Usage: Search Engine Market Share: All Platforms: Seznam data is updated daily, averaging 0.000 % from Nov 2024 (Median) to 28 Nov 2024, with 5 observations. The data reached an all-time high of 0.040 % in 28 Nov 2024 and a record low of 0.000 % in 27 Nov 2024. Peru Internet Usage: Search Engine Market Share: All Platforms: Seznam data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Peru – Table PE.SC.IU: Internet Usage: Search Engine Market Share.

The Internet has become one of the main sources of information for university students’ learning. Since anyone can disseminate content online, however, the Internet is full of irrelevant, biased, or even false information. Thus, students’ ability to use online information in a critical-reflective manner is of crucial importance. In our study, we used a framework for the assessment of students’ critical online reasoning (COR) to measure university students’ ability to critically use information from online sources and to reason on contentious issues based on online information. In addition to analyzing students’ COR by evaluating their open-ended short answers, we also investigated the students’ web search behavior and the quality of the websites they visited and used during this assessment. We analyzed both the number and type of websites as well as the quality of the information these websites provide. Finally, we investigated to what extent students’ web search behavior as well as the quality of the used website contents are related to higher task performance. To investigate this question, we used five computer-based performance tasks and asked 160 students from two German universities to perform a time-restricted open web search to respond to the open-ended questions presented in the tasks. The written responses were evaluated by two independent human raters. To analyze the students’ browsing history, we developed a coding manual and conducted a quantitative content analysis for a subsample of 50 students. The number of visited webpages per participant per task ranged from 1 to 9. Concerning the type of website, the participants relied especially on established news sites and Wikipedia. For instance, we found that the number of visited websites and the critical discussion of sources provided on the websites correlated positively with students’ scores. The identified relationships between students’ web search behavior, their performance in the CORA tasks, and the qualitative website characteristics are presented and critically discussed in terms of limitations of this study and implications for further research.

Among the 20 most popular websites in Russia, Google.com had the highest average monthly traffic, at over five billion visits in November 2024. It was followed by the Russian search engine Yandex.ru, with around 3.5 billion visits in the same period.

Data related to the Master Thesis on The Impact of Biased Search Results on User Engagement in Web Search. The dataset consists of search results, search result annotations, interaction logs of the final study and the survey responses for the pilot and final study.

Search Engine Optimization Services Market Outlook

The global market size for Search Engine Optimization (SEO) Services was valued at approximately $65 billion in 2023 and is projected to reach around $150 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.5%. This robust growth can be attributed to various factors, including the increasing emphasis on digital marketing, the rise in online content, and the growing need for businesses to improve their online visibility and search engine rankings.

One of the primary growth drivers for the SEO services market is the exponential increase in internet usage and the proliferation of digital content. As more consumers turn to the internet for information, entertainment, and shopping, businesses recognize the critical importance of appearing prominently in search engine results. Consequently, companies are increasingly investing in SEO services to enhance their online visibility, attract more visitors to their websites, and ultimately drive higher conversion rates. Furthermore, the rise of social media platforms and mobile internet usage has also underscored the need for comprehensive SEO strategies that encompass various digital channels.

Another significant factor contributing to the market's growth is the continuous evolution of search engine algorithms. Search engines like Google are constantly updating their algorithms to deliver more relevant and high-quality results to users. These updates often necessitate businesses to adapt their SEO strategies to maintain or improve their rankings. This evolving landscape creates a sustained demand for specialized SEO services that can help businesses navigate these changes effectively. Additionally, the increasing complexity of SEO, which now involves a mix of technical expertise, content creation, and analytics, has led many enterprises to seek professional SEO services rather than relying solely on in-house efforts.

The rise of e-commerce and the digital transformation of various sectors, including healthcare, finance, and education, have also bolstered the demand for SEO services. As more businesses and industries move online, the need to stand out in a crowded digital marketplace becomes even more critical. SEO services play a vital role in helping businesses achieve higher search engine rankings, reach their target audiences more effectively, and compete successfully in the digital space. Moreover, the growing importance of local SEO, driven by the increasing use of mobile search and location-based queries, has further fueled the market's expansion.

Regionally, North America remains the largest market for SEO services, driven by the high concentration of digital-savvy businesses and the advanced state of the e-commerce sector. The region is expected to maintain its dominance over the forecast period, although Asia Pacific is anticipated to exhibit the highest growth rate. The rapid digitalization in countries like China and India, coupled with the increasing penetration of the internet and smartphones, is propelling the demand for SEO services in the region. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by the ongoing digital transformation across various industries.

Service Type Analysis

The SEO services market is segmented by service type, including On-Page SEO, Off-Page SEO, Technical SEO, Local SEO, Content SEO, and Others. Each of these service types addresses different aspects of SEO and together contribute to a comprehensive strategy for optimizing a website's performance.

On-Page SEO focuses on optimizing individual web pages to rank higher and earn more relevant traffic in search engines. This includes optimizing content, HTML source code, and media. It plays a crucial role as it directly impacts the visibility of the content. Factors such as meta tags, keyword density, and internal linking are critical components of On-Page SEO. As search engines become more sophisticated, On-Page SEO has evolved to include user experience elements such as page load speed and mobile friendliness.

Off-Page SEO involves activities performed outside the website to improve its ranking. This primarily includes building high-quality backlinks from authoritative websites, which act as endorsements for the website's content. Social media marketing, influencer outreach, and guest blogging are also key components of Off-Page SEO. The growing importance of backlinks in search engine algorithms has led to higher investments in Off-Page SEO services, making t

High order profile expansion dataset consisting of 8,362 items and 478,458 users, who have done 48,715,350 ratings between 1 and 5 both inclusive.

The Web Portal Operation industry is highly concentrated, with three companies controlling almost the entire industry; the largest company in the industry, Alphabet Inc, has a market share greater than 90% in 2025. This market concentration has fostered significant advertising revenue but made it exceedingly difficult for smaller web portals to survive. Yet, the presence of local champions like Yandex in Russia and Seznam in the Czech Republic demonstrates that regional portals can find niches, particularly where differentiated content or national digital policies shape market dynamics. Search engines generate most, if not all, of their revenue from advertising. Technological growth has led to more households being connected to the internet and a boom in e-commerce has made the industry increasingly innovative. Over the past decade, a boost in the percentage of households with internet access across Europe has supported revenue expansion, while strengthening technological integration with daily life has boosted demand for web portals. Industry revenue is expected to swell at a compound annual rate of 17.4% over the five years through 2025, including growth of 15% in 2025, to reach €74.9 billion. While profit is high, it is projected to dip amid hiking operational pressures, changing advertising dynamics and heightened regulatory compliance costs. A greater proportion of transactions being carried out online has driven innovation in targeted digital advertising, with declines in rival advertising formats like print media and television expanding the focus on digital marketing as a core strategy. Market leaders have maintained dominance via exclusive agreements, like Google’s multi-billion-euro deals to remain the default search engine on Apple and Android devices, embedding themselves deeper into users’ daily digital interactions. At the same time, the rise of privacy-first search engines like DuckDuckGo, Ecosia and Qwant reflects shifting consumer attitudes toward data privacy and environmental impact. However, Google's status as the default search provider on most mainstream platforms, coupled with robust integration through Chrome and Google's broader ecosystem, has significantly constrained market entry for competitors, perpetuating the industry’s concentration. The rise of the mobile advertising market and the proliferation of mobile devices mean there are plenty of opportunities for search engines, which are expected to capitalise on these trends further moving forward. Smartphones could disrupt the industry's status quo, as the rising popularity of devices that don’t use Google as the default engine benefits other web portals. Technological advancements that incorporate user data are likely to make it easier to tailor advertisements and develop new ways of using consumer data. Initiatives like the European Search Perspective (EUSP) joint venture between Ecosia and Qwant signal the beginnings of intensified competition, especially around privacy and regional digital sovereignty. Nonetheless, industry growth is set to continue, fuelled by surging demand for localised, targeted digital advertising and heightened investment in mobile marketing. Industry revenue is forecast to jump at a compound annual rate of 20.4% over the five years through 2030 to reach €189.7 billion.

Polish Wikipedia articles with "Cite web" templates linking to celebrity gossip blogs and websites.

The Webis Query Segmentation Corpus 2010 (Webis-QSeC-10) contains segmentations for 53,437 web queries obtained from Mechanical Turk crowdsourcing (4,850 used for training in our CIKM 2012 paper). For each query, at least 10 MTurk workers were asked to segment the query. The corpus represents the distribution of their decisions. We provide the training and test sets as single folders in Zip archives containing several files. The files "...-queries.txt" contain the query strings and a unique ID for each query. The files "...-segmentations-crowdsourced.txt" contain the crowdsourced segmentations with their number of votes per query ID (see below for an example). The "data" folders contain all the data (n-gram frequencies, PMI values, POS tags, etc.) needed to replicate the evaluation results of our proposed segmentation algorithms. For convenience reasons, the folder "segmentations-of-algorithms" contain the segmentations that our proposed algorithms compute. The original queries were extracted from the AOL query log, and range from 3 to 10 keywords in length. For each query at least 10 MTurk workers were asked to segment the query and their decisions are accumulated in the corpus. The examples below demonstrate two different cases. Sample queries with internal ID (as in "Webis-QSeC-10-training-set-queries.txt"): 2315313155 harvard community credit union 1858084875 women's cycling tops Sample segmentations (as in "webis-qsec-10-training-set-segmentations-crowdsourced.txt"): 2315313155 [(6, 'harvard community credit union'), (2, 'harvard community|credit union'), (1, 'harvard|community|credit union'), (1, 'harvard|community credit union')] 1858084875 [(5, "women's|cycling tops"), (2, "women's|cycling|tops"), (2, "women's cycling|tops"), (1, "women's cycling tops")] Each query has a unique internal ID (e.g., 2315313155 in the first example) and the segmentations file contains at least 10 different decisions the MTurk workers made for that query. In the first example, 6 workers have all 4 keywords in one segment, 2 workers decided to break after the second word (denoted by a |) etc. Note that apostrophe in the second example (query ID 1858084875) is escaped by double quotes around the segmentation strings. {"references": ["Matthias Hagen, Martin Potthast, Benno Stein, and Christof Br\u00e4utigam. The Power of Na\u00efve Query Segmentation. In Fabio Crestani et al, editors, 33rd International ACM Conference on Research and Development in Information Retrieval (SIGIR 10), pages 797-798, July 2010. ACM. ISBN 978-1-4503-0153-4."]}

Internet Usage: Search Engine Market Share: Mobile: Start Page data was reported at 0.000 % in 04 Sep 2024. This stayed constant from the previous number of 0.000 % for 03 Sep 2024. Internet Usage: Search Engine Market Share: Mobile: Start Page data is updated daily, averaging 0.000 % from Feb 2024 (Median) to 04 Sep 2024, with 199 observations. The data reached an all-time high of 8.330 % in 28 May 2024 and a record low of 0.000 % in 04 Sep 2024. Internet Usage: Search Engine Market Share: Mobile: Start Page data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Kiribati – Table KI.SC.IU: Internet Usage: Search Engine Market Share.

METHODS

Topic determination

The project was developed as a team science exercise during a course on Nutrient Biology (New Mexico Institute of Mining and Technology, New Mexico, USA; BIOL 4089/5089). Students were all women pursuing degrees in Biology and Earth Science, with extensive internet search acumen developed from coursework and personal experience. We (students and professor) devoted ~5 hours to discussing women’s health topics prior to searching, defining search criteria, and developing a scoring system. These discussions led to a list of 12, non-cancer health topics particular to women’s health associated with human cis-gender female biology. Considerations of transgender health were discussed, with the consensus decision that those issues are scientifically relevant but deserving of a separate analysis not included here.

Search protocol

After agreeing on search terms, we experimented with settings in the Advanced Search feature in Google (www.google.com), and collectively agreed to the following settings: Language (English); search terms appearing in the “text” of the page; ANY of the terms “woman”, “women” ,“female”; ALL terms when using a single topic from list above with the addition of the word “nutrient”. Figure 1 shows a screenshot for how a search was conducted for endometriosis as an example. To standardize data collection among investigators, all results from the first 5 pages of results were collected. Search result URLs were followed, where a suite of data were gathered (variables in Table 2) and entered into a shared database (Appendix 1). Definitions for each variable (Table 2) were articulated following a 1-week trial period and further group discussion. Variables were defined to minimize subjectivity across investigators, clarify the reporting of results, and standardize data collection.

Scoring metric

The scoring metric was developed to allow for mean and variation (standard deviation, SD; standard error, SE) to be calculated from each topic, and compare among topics, and answer how much variation in quality is likely to be encountered across categories of women’s health issues. We report both variation metrics as SD encompasses the variation of the data set, while SE scales for sample size variation among categorical variables. When searching topics using the same criteria:

1. Are some topics more likely to result in results for pages with scientifically verifiable information?

1. Does the variation of quality vary between topics?

Peer-reviewed journal articles were included in the database if encountered in the searches but were removed before statistical analysis. The justification for removing those sources was that it is possible the Google algorithm included those sources disproportionately for our group of college students and a professor who regularly searches for academic articles. We also assume those sources are consulted less frequently by lay audiences searching for health information.

Scores were based on six binary (presence/absence) attributes of each web page evaluated. These were: Author (name present/absent), author credentials given, reviewer, reviewer credentials, sources listed, peer-reviewed sources listed. A score of 1 was given if the attribute was present, and 0 if absent. The total number of references cited on a webpage, as well as the number of those that were peer-reviewed (Table 2) were recorded, but for scoring purposes, a 1 or 0 was assigned if there were or were not references and peer-reviewed references, respectively. Potential scores thus ranged from 0 to 6.

We performed a simple validation experiment via anonymous surveys sent to students at our institution (New Mexico Tech), a predominantly STEM-focused public university. Using the final scores from the search result webpages, a single website from each score was selected at random using the RAND() function in Microsoft Excel to assign a random variable as an identifier to each URL, then sorting by that variable and selecting the first article in a given score category. Webpages with scores of 0 or 6 were excluded from the validation experiment. Following institutional review, a survey was sent to the “all student” email list, and recipients were directed to a web survey that asked participants to give a score of 1-5 to each of the 5 random (but previously scored) web pages, without repeating a score. Participants were given minimal information about the project and had no indication the pages had already been assigned scores. Survey results were collected anonymously by having responses routed to a spreadsheet, and no personally identifiable data were collected from participants.

Statistical analysis

Differences in mean scores within each health topic and the mean number of sources per evaluated webpage were evaluated by calculating Bayes Factors; response variables (mean score, number of sources) for each topic were compared to a null model of no difference across topics (y ~ category + error). Equal prior weight was given to each potential model. Variance inequality was tested via Levene’s test, and normality was assessed using quartile-quartile plots. Correlation analysis was used to test the strength of the association between individual scores per website and the number of sources cited per website. Because only the presence or absence of sources was considered in the score calculation, the number of sources is independent of score, and justifies correlation analysis. Statistical analyses were conducted in the open-source software package JASP version 0.19.2 (JASP, 2024).