General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

The Alternative Data Market size was valued at USD 7.20 billion in 2023 and is projected to reach USD 126.50 billion by 2032, exhibiting a CAGR of 50.6 % during the forecasts period. Recent developments include: In April 2023, Thinknum Alternative Data launched new data fields to its employee sentiment datasets for people analytics teams and investors to use this as an 'employee NPS' proxy, and support highly-rated employers set up interviews through employee referrals. , In September 2022, Thinknum Alternative Data announced its plan to combine data Similarweb, SensorTower, Thinknum, Caplight, and Pathmatics with Lagoon, a sophisticated infrastructure platform to deliver an alternative data source for investment research, due diligence, deal sourcing and origination, and post-acquisition strategies in private markets. , In May 2022, M Science LLC launched a consumer spending trends platform, providing daily, weekly, monthly, and semi-annual visibility into consumer behaviors and competitive benchmarking. The consumer spending platform provided real-time insights into consumer spending patterns for Australian brands and an unparalleled business performance analysis. .

The global market for website analytics and competitor analysis tools is experiencing robust growth, projected to reach $[Estimate based on available data, e.g., $5 billion] in 2025, with a Compound Annual Growth Rate (CAGR) of [Estimate, e.g., 12%] from 2025 to 2033. This expansion is driven by the increasing reliance of businesses, both large enterprises and SMEs, on data-driven decision-making for improved marketing strategies, website optimization, and competitive intelligence. Key trends shaping this market include the rising adoption of AI-powered analytics for deeper insights, the integration of website analytics with other marketing platforms, and the growing demand for comprehensive solutions that cover SEO, PPC, and social media analytics. While the market faces some restraints, such as the complexity of some analytics tools and the increasing cost of premium features, the overall growth trajectory remains positive. The competitive landscape is highly dynamic, with established players like Google, SEMrush, and SimilarWeb dominating the market through their comprehensive offerings and extensive user bases. However, smaller, specialized companies like BuiltWith, SpyFu, and WooRank are carving out niches for themselves by focusing on specific areas of website analytics or offering unique functionalities. The competitive intensity is driving innovation, leading to the development of more user-friendly interfaces, enhanced reporting capabilities, and improved data visualization tools. The market is also witnessing the emergence of new players offering innovative solutions leveraging cutting-edge technologies, promising further disruption and shaping the future of competitor analysis. Regional variations exist, with North America and Europe currently leading the market, but strong growth is expected from Asia-Pacific, particularly from countries like India and China, as digital adoption continues to accelerate.

Discover the booming Advertising Intelligence Tool market! Explore key trends, leading companies like Semrush & SimilarWeb, and projected growth to 2033. Learn how AI-powered insights are transforming digital advertising strategies and maximizing ROI.

The booming Digital Ad Intelligence Software market is projected to reach $45 billion by 2033, growing at a 15% CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (Pathmatics, SimilarWeb, etc.), and regional insights. Discover how data-driven decision-making is transforming digital advertising.

Определение: Ежемесячный трафик на 15 сайтов с искусственным интеллектом со стационарных и мобильных компьютеров в каждой стране. [Переведено с en: английского языка] Тематическая область: Информационно-коммуникационные технологии [Переведено с en: английского языка] Область применения: Искусственный интеллект [Переведено с en: английского языка] Единица измерения: Количество посещений [Переведено с en: английского языка] Примечание: Similarweb не предоставляет точных данных о количестве посещений веб-сайтов, которые посещают менее 5000 человек. В этих случаях используется приблизительная оценка в 4999 посещений. [Переведено с es: испанского языка] Источник данных: Цифровая обсерватория Десарролло (ODD) на основе Similarweb [Переведено с es: испанского языка] Последнее обновление: Feb 12 2024 9:00AM Организация-источник: Экономическая комиссия по Латинской Америке и Карибскому бассейну [Переведено с en: английского языка] Definition: Monthly traffic to 15 artificial intelligence sites from fixed and mobile computers per country. Thematic Area: Information and Communication Technologies Application Area: Artificial intelligence Unit of Measurement: Number of visits Note: Similarweb does not provide an exact number of visits for websites that receive fewer than 5,000 visits. In these cases, an approximate estimate of 4,999 is used. Data Source: Observatorio de Desarrollo Digital (ODD) based on Similarweb Last Update: Feb 12 2024 9:00AM Source Organization: Economic Comission for Latin America and the Caribbean

Puff Bar, a disposable electronic nicotine delivery system (ENDS), was the ENDS brand most commonly used by U.S. youth in 2021. We explored whether Puff Bar’s rise in marketplace prominence was detectable through advertising, retail sales, social media, and web traffic data sources. We retrospectively documented potential signals of interest in and uptake of Puff Bar in the United States using metrics based on advertising (Numerator and Comperemedia), retail sales (NielsenIQ), social media (Twitter, via Sprinklr), and web traffic (Similarweb) data from January 2019 to June 2022. We selected metrics based on (1) data availability, (2) potential to graph metric longitudinally, and (3) variability in metric. We graphed metrics and assessed data patterns compared to data for Vuse, a comparator product, and in the context of regulatory events significant to Puff Bar. The number of Twitter posts that contained a Puff Bar term (social media), Puff Bar product sales measured in dollars (sales), and the number of visits to the Puff Bar website (web traffic) exhibited potential for surveilling Puff Bar due to ease of calculation, comprehensibility, and responsiveness to events. Advertising tracked through Numerator and Comperemedia did not appear to capture marketing from Puff Bar’s manufacturer or drive change in marketplace prominence. This study demonstrates how quantitative changes in metrics developed using advertising, retail sales, social media, and web traffic data sources detected changes in Puff Bar’s marketplace prominence. We conclude that low-effort, scalable, rapid signal detection capabilities can be an important part of a multi-component tobacco surveillance program.

Women in Headlines: Bias

Investigating Gendered Language, Temporal Trends, and Themes

By Amber Thomas [source]

About this dataset

This dataset contains all of the data used in the Pudding essay When Women Make Headlines published in January 2022. This dataset was created to analyze gendered language, bias and language themes in news headlines from across the world. It contains headlines from top50 news publications and news agencies from four major countries - USA, UK, India and South Africa - as published by SimilarWeb (as of 2021-06-06).

To collect this data we used RapidAPI's google news API to query headlines containing one or more of keywords selected based on existing research done by Huimin Xu & team and The Swaddle team. We analyzed words used in headlines manually curating two dictionaries — gendered words about women (words that are explicitly gendered) and words that denote societal/behavioral stereotypes about women. To calculate bias scores, we utilized technology developed through Yasmeen Hitti & team’s research on gender bias text analysis. To categorize words used into themes (violence/crime, empowerment, race/ethnicity/identity etc), we manually curated four dictionaries utilizing Natural Language Processing packages for Python like spacy & nltk for our analysis. Plus, inverting polarity scores with vaderSentiment algorithm helped us shed light on differences between women-centered/non-women centered polarity levels as well as differences between global polarity baselines of each country's most visited publications & news agencies according to SimilarWeb 2020 statistics..

This dataset enables journalists, researchers and educators researching issues related to gender equity within media outlets around the world further insights into potential disparities with just a few lines of code! Any discoveries made by using this data should provide valuable support for evidence-based argumentation . Let us advocate for greater awareness towards female representation better quality coverage!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive look at the portrayal of women in headlines from 2010-2020. Using this dataset, researchers and data scientists can explore a range of topics including language used to describe women, bias associated with different topics or publications, and temporal patterns in headlines about women over time.

To use this dataset effectively, it is helpful to understand the structure of the data. The columns include headline_no_site (the text of the headline without any information about which publication it is from), time (the date and time that the article was published), country (the country where it was published), bias score (calculated using Gender Bias Taxonomy V1.0) and year (the year that the article was published).

By exploring these columns individually or combining them into groups such as by publication or by topic, there are many ways to make meaningful discoveries using this data set. For example, one could explore if certain news outlets employ more gender-biased language when writing about female subjects than other outlets or investigate whether female-centric stories have higher/lower bias scores than average for a particular topic across multiple countries over time. This type of analysis helps researchers to gain insight into how our culture's dialogue has evolved over recent years as relates to women in media coverage worldwide

Research Ideas

• A comparative, cross-country study of the usage of gendered language and the prevalence of gender bias in headlines to better understand regional differences.
• Creating an interactive visualization showing the evolution of headline bias scores over time with respect to a certain topic or population group (such as women).
• Analyzing how different themes are covered in headlines featuring women compared to those without, such as crime or violence versus empowerment or race and ethnicity, to see if there’s any difference in how they are portrayed by the media

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: headlines_reduced_temporal.csv | Column name | Description | |:---------------------|:-------------------------------------------------------------------------------------...

This statistic shows the leading online dating websites in the Netherlands as of January 2017, based on the number of visitors per month. The source mentions that dating websites in the Netherlands do not provide this information and the data comes from intelligence agency Similarweb. As of January 2017, Lexa.nl was the most popular online dating website in the Netherlands, with 426,000 monthly visitors.

During the second half of 2017, roughly 17 percent of the Dutch internet users indicated they visited an online dating website, service or app. Users aged 16 to 24 years did this the most: approximately 22 percent of all users in this age group indicated they did so.