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

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

The dataset used in the paper

Host country of organization for 86 websites in study.

Products and rating of e-commerce website NewChic.com

Foreword

This dataset is a preview of a bigger dataset.

My Telegram bot will answer your queries and allow you to contact me. Whether you want updated data (2022), english listings or a custom requests, you can reach out through the bot.

Content

Product records and their popularity / interactions with customers.

Data snapshot on early august 2020.

Inspiration

Among other opportunities, you may use this data for ...

RESEARCH opportunities - to find trends of products that do not age - top products - user tastes - study the segmentation of user tastes and link that to the population of visitors

BUSINESS opportunities

• Finding top products to sell

  • => optimise your stock by picking styles people want

• Finding top categories of products

  • which niches might sell the most products ? That way you may focus your investments and not waste funds on dead categories.

About

Sentiment analysis is one of the most common application area in NLP. However, finding the dataset for low-resource languages like Turkish can be sometimes challenging. Here, you can find beyazperde.com user comments data in order to train your sentiment analysis models. Beyazperde.com is quite similar to imdb.com and you can read the users' opinions about the related films. The website has almost 5.5 million visitors monthly.

Original dataset (Demirtas and Pechenizkiy, 2013) have 10662 comments with their respective classes ranging from 1 to 5. The star ratings of comments divided into 2 classes. 4 or 5-star reviews are taken as positive reviews; while 1 or 2-star reviews are considered negative. Reviews with 3-stars are excluded.

The final data is perfectly balanced. In the training data, there are 3998 positive labelled and 3998 negative labelled comments. When it comes to the test data, there are 1333 comments for each class.

Content

The structure of the data is pretty simple. There are 2 columns which are user comments and their respective class. 0 denotes for negative class and 1 denotes for positive.

Acknowledgement

The Turkish movie reviews dataset was introduced by Demirtas and Pechenizkiy (2013).

Reference

Erkin Demirtas and Mykola Pechenizkiy. Cross-Lingual Polarity Detection with Machine Translation. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, page 9. ACM, 2013.

If you have any further questions regarding to the dataset, please feel free to ask.

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!

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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 | |:---------------------|:-------------------------------------------------------------------------------------...

Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

Website type for the 86 websites in study.

This dataset contains 4 parts. "SimilarWeb dataset with screenshots" is created by scraping web elements, their CSS, and corresponding screenshots in three different time intervals for around 100 web pages. Based on this data, the "SimilarWeb dataset with SSIM column" is created with the target column containing the structural similarity index measure (SSIM) of the captured screenshots. This part of the dataset is used to train machine learning regression models. To evaluate approach, "Accessible web pages dataset" and "General use web pages dataset" parts of the dataset are used.

Определение: Общий 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 9 2024 1:04PM Организация-источник: Экономическая комиссия по Латинской Америке и Карибскому бассейну [Переведено с en: английского языка] Definition: Total 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 9 2024 1:04PM Source Organization: Economic Comission for Latin America and the Caribbean

Comprehensive dataset analyzing eBay's daily visitor traffic patterns, geographic distribution, device usage, and competitive positioning based on third-party analytics from Similarweb and Semrush.

Industry vertical of organization for 86 websites in study.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Similarweb (SMWB) Stock Price Outlook Sees Shift

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.

Description of the Report and Topic Justification

Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.

In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).

One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.

In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.

Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
- For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015). - McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013). - Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979). -Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and S...

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.

Определение: Измерение и классификация потока посетителей или пользователей веб-сайтов на основе различных категорий или тематик. Это включает в себя анализ и категоризацию веб-трафика с точки зрения областей или типов контента, который посетители ищут или потребляют. [Переведено с en: английского языка] Тематическая область: Информационно-коммуникационные технологии [Переведено с en: английского языка] Область применения: Веб-трафик [Переведено с en: английского языка] Примечание: Веб-трафик охватывает активность как на настольных компьютерах, так и на мобильных устройствах. Категория электронной коммерции включает трафик на сайты, относящиеся к категории электронной коммерции и торговых площадок. [Переведено с es: испанского языка] Источник данных: Цифровая обсерватория Десарролло (ODD) на основе Similarweb [Переведено с es: испанского языка] Последнее обновление: Jan 31 2024 6:02PM Организация-источник: Экономическая комиссия по Латинской Америке и Карибскому бассейну [Переведено с en: английского языка] Definition: Measurement and classification of the flow of visitors or users on websites based on different categories or topics. This involves analyzing and categorizing web traffic in terms of the areas or types of content that visitors are seeking or consuming. Thematic Area: Information and Communication Technologies Application Area: Web traffic Note: Web traffic encompasses both desktop and mobile device activity. The e-Commerce category includes traffic to sites categorized as e-Commerce and Marketplaces. Data Source: Observatorio de Desarrollo Digital (ODD) based on Similarweb Last Update: Jan 31 2024 6:02PM Source Organization: Economic Comission for Latin America and the Caribbean