A complete list of live websites using the Similar Posts Ai Spai technology, compiled through global website indexing conducted by WebTechSurvey.

A complete list of live websites using the Similar Posts Ontology technology, compiled through global website indexing conducted by WebTechSurvey.

In August 2025, Google.com was the most visited website worldwide, with an average of 98.2 billion monthly visits. The platform has maintained its leading position since June 2010, when it surpassed Yahoo to take first place. YouTube ranked second during the same period, recording over 48 billion monthly visits. 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.

Context

This dataset was created by scraping different websites and then classifying them into different categories based on the extracted text.

Content

Below are the values each column has. The column names are pretty self-explanatory. website_url: URL link of the website. cleaned_website_text: the cleaned text content extracted from the

The dataset consists of images and their respective yolo labels for bounding box prediction. There are 144 classes which are predicted and are mentioned in the data.yaml file.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

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: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

Context

This dataset includes some of the basic information of the websites we daily use. While scrapping this info, I learned quite a lot in R programming, system speed, memory usage etc. and developed my niche in Web Scrapping. It took about 4-5 hrs for scrapping this data through my system (4GB RAM) and nearly about 4-5 days working out my idea through this project.

Content

The dataset contains Top 50 ranked sites from each 191 countries along with their traffic (global) rank. Here, country_rank represent the traffic rank of that site within the country, and traffic_rank represent the global traffic rank of that site.

Since most of the columns meaning can be derived from their name itself, its pretty much straight forward to understand this dataset. However, there are some instances of confusion which I would like to explain in here:

1) most of the numeric values are in character format, hence, contain spaces which you might need to clean on.

2) There are multiple instances of same website. for.e.g. Yahoo. com is present in 179 rows within this dataset. This is due to their different country rank in each country.

3)The information provided in this dataset is for the top 50 websites in 191 countries as on 25th May 2017 and is subjected to change in future time due to the dynamic structure of ranking.

4) The dataset inactual contains 9540 rows instead of 9550(50*191 rows). This was due to the unavailability of information for 10 websites.

PS: in case if there are anymore queries, comment on this, I'll add an answer to that in above list.

Acknowledgements

I wouldn't have done this without the help of others. I've scrapped this information from publicly available (open to all) websites namely: 1) http://data.danetsoft.com/ 2) http://www.alexa.com/topsites , of which i'm highly grateful. I truly appreciate and thanks the owner of these sites for providing us with the information that I included today in this dataset.

Inspiration

I feel that there this a lot of scope for exploring & visualization this dataset to find out the trends in the attributes of these websites across countries. Also, one could try predicting the traffic(global) rank being a dependent factor on the other attributes of the website. In any case, this dataset will help you find out the popular sites in your area.

The share of individuals watching paid content on websites like Netflix and HBO in Norway generally increased from 2009 to 2020. In 2009, the share amounted to three percent of respondents, whereas in 2020 it reached ** percent.

and-just-like-that.org is ranked #4353 in RU with 338.19K Traffic. Categories: . Learn more about website traffic, market share, and more!

same.energy is ranked #78327 in US with 323.15K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

The Japanese review site my-best.com had the highest bounce rate among the most visited retail websites in Japan in July 2024. Operated by mybest, Inc. and part of LY Corporation, the website had a bounce of nearly ** percent, while ranking as the ****** most visited retail website in the same month.

This is a dataset containing the URL and the text content of it of 13.5k websites. It contains 9 different class in total. The website's text content is not preprocessed, so anyone who wants can do it's own preprocessing.

same-witness.com is ranked #0 in PH with 7.63M Traffic. Categories: . Learn more about website traffic, market share, and more!

same.new is ranked #25576 in IN with 545.43K Traffic. Categories: . Learn more about website traffic, market share, and more!

i-like-seen.com is ranked #904 in JP with 3.86M Traffic. Categories: . Learn more about website traffic, market share, and more!

Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.

Key Features:

LLM-generated annotations: Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.

Improved multi-label classification: Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.

Multilingual applicability: The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.

Dataset Composition:

curlie-gpt3.5-10k: 10,000 websites labeled using GPT-3.5, context 2 and 1-shot

curlie-gpt4-10k: 10,000 websites labeled using GPT-4, context 2 and zero-shot

Intended Use:

Fine-tuning and advancing Homepage2Vec or similar website classification models

Research on LLM-generated datasets for text classification tasks

Exploration of multilingual website classification

Additional Information:

Project and report repository: https://github.com/CS-433/ml-project-2-mlp

Acknowledgments:

This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with Prof. Robert West and Tiziano Piccardi.

AI Powered Website Builder Market Outlook

The global AI powered website builder market size was valued at USD 1.5 billion in 2023 and is forecasted to reach USD 7.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. The market is witnessing significant growth due to the increasing demand for cost-effective and efficient website creation solutions, the rising adoption of AI technology across various industries, and the growing trend of digitalization. Small and medium enterprises (SMEs) are particularly driving this demand as they seek to establish a strong online presence without incurring excessive costs.

The major growth factor for the AI powered website builder market is the need for businesses and individuals to have a professional online presence. In today's digital age, having a website is no longer optional but a necessity. AI-powered website builders offer a cost-effective and user-friendly solution to create and maintain websites, thereby attracting a wide range of users from different segments. The ability to generate website layouts, content, and even SEO recommendations automatically makes these tools incredibly valuable, particularly for those who lack technical skills. This trend is particularly strong among small businesses and startups that need to establish their presence quickly and affordably.

Another significant factor contributing to the market's growth is the rapid advancement in AI technologies. Innovations in machine learning, natural language processing, and computer vision are enabling website builders to offer more sophisticated features such as voice-activated commands, personalized content suggestions, and automated customer service chatbots. These advancements make it easier for users to create highly functional and aesthetically pleasing websites, thus driving the adoption of AI-powered website builders. Furthermore, the integration of AI with other emerging technologies like blockchain and IoT offers new avenues for enhancing website functionality and security, adding further momentum to market growth.

Additionally, the COVID-19 pandemic has accelerated the digital transformation of businesses, leading to an increased reliance on online platforms. As a result, there has been a surge in demand for AI-powered website builders, as businesses aim to reach their customers through digital means. The pandemic has highlighted the importance of having a robust online presence, and AI-powered website builders provide a quick and efficient solution to meet this need. This shift towards digital platforms is expected to sustain even post-pandemic, providing a long-term growth trajectory for the market.

On the regional front, North America holds the largest market share due to the high adoption rate of advanced technologies and the presence of key market players. The region's robust IT infrastructure and the increasing demand for AI-driven solutions from various industries further fuel market growth. Meanwhile, Asia Pacific is expected to witness the highest growth rate during the forecast period. The region's burgeoning startup ecosystem, coupled with increasing internet penetration and digital literacy, creates a fertile ground for the expansion of AI-powered website builders. Countries like China, India, and Japan are leading this growth, driven by their large consumer base and favorable government initiatives supporting digitalization.

Component Analysis

The AI powered website builder market can be segmented by component into software and services. Software solutions constitute the core of AI-powered website builders, offering a wide range of functionalities from basic website creation tools to advanced features like AI-driven design suggestions, content optimization, and SEO recommendations. These software solutions are typically characterized by their ease of use and accessibility, enabling users with minimal technical skills to create professional-grade websites. The software segment is expected to maintain a dominant position in the market, driven by continuous advancements in AI technology and the growing demand for intuitive, user-friendly website creation tools.

Services, on the other hand, complement the software offerings by providing additional support and expertise to users. These services may include customer support, training and consultancy, customization services, and ongoing maintenance and updates. As businesses increasingly adopt AI-powered website builders, the demand for these ancillary services is also rising. Users often seek professional guidance to maximize

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

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Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

A complete list of live websites using the Like technology, compiled through global website indexing conducted by WebTechSurvey.