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.

In the end of 2020, baidu.com was the most popular Chinese website with an estimated average daily usage time of about **** minutes and ** seconds per visitor. The e-commerce websites tmall.com and taobao.com followed with about ***** minutes and *** minutes respectively.

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

This is an instance of the LabPal experimental environment that processes data files about the structure of web sites.

The lab contains results of a large-scale survey of websites, in order to measure various features related to their size and structure: DOM tree size, maximum degree, depth, diversity of element types and CSS classes, among others. The goal of this research is to serve as a reference point for studies that include an empirical evaluation on samples of web pages.

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

A complete list of live websites using the tsparticles-preset-big-circles technology, compiled through global website indexing conducted by WebTechSurvey.

Longitudinal data from observations in 2009, 2014, and 2019 in a large panel survey of websites of small and medium companies in two European countries. 658 company websites were registered in all three panel waves.

A complete list of live websites using the react-big-calendar technology, compiled through global website indexing conducted by WebTechSurvey.

In 2020, the news website digi24.ro and adevarul.ro had the highest online advertising budget in Romania, each totaling over ************ euros. The official website of the online marketplace OLX ranked sixth, with an online ad budget worth **** million euros.

In March 2025, ******** accounted for ** percent of all social media site visits in the United States, confirming its position as the leading social media website by far. Other social media platforms, despite their popularity, had to make do with smaller shares of visits across desktop, mobile, and tablet devices combined. ********* ranked second with ***** percent of all U.S. social media site visits, while X (previously Twitter) accounted for ***** percent of the total visits in the country. Additionally, the U.S. is home to the third largest social media audience worldwide. Facebook: mobile vs desktop usage At the beginning of 2022, around ** percent of Facebook users across the globe were using the platform’s social networking services exclusively via mobile phone, while only *** percent reported using their desktop or laptop devices. In September 2022, three Facebook Inc. products occupied some of the leading positions as most downloaded social networking apps on the Apple App Store in the United States. WhatsApp’s messaging platform ranked second with more than *** million downloads, while Facebook and the instant-messaging service Messenger followed ranking third and fifth with *** million and **** million downloads respectively. Social media evolution Between 2012 and 2024, the daily time spent on social networks worldwide experienced an almost constant increase, with users reaching an average of *** minutes per day in 2023, with a decrease to *** daily minutes of engagement in 2024. However, users’ favorite platforms have changed since 2019, and the power balance appears to be shifting further from Facebook’s market dominance. Not only Facebook’s user growth rate is estimated to slow down in the next years, but users belonging to Generation Z appear to prefer video-first social platforms like Snapchat, TikTok, and YouTube.

The dataset contains and processes results of a large-scale survey of 708 websites, made in December 2019, in order to measure various features related to their size and structure: DOM tree size, maximum degree, depth, diversity of element types and CSS classes, among others. The goal of this research is to serve as a reference point for studies that include an empirical evaluation on samples of web pages.

See the Readme.md file inside the archive for more details about its contents.

A study released in March 2025 that looked at about 35,000 websites found that online search channels were responsible for almost ** percent of the traffic generated to these domains. By the time of this study, direct traffic corresponded to around **** percent of visits to the analyzed websites. Meanwhile, large language models (LLMs) like ChatGPT and Gemini corresponded to around *** percent of the verified traffic, representing a share just below e-mail platforms.

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#face2face

Website Builders Market was valued at USD 1.97 Billion in 2023 and is expected to reach USD 3.58 Billion in 2031, growing at a CAGR of 7.73% over the forecast period of 2024 to 2031.Key Market DriversIncreasing adoption of e-commerce platforms by small and medium enterprises (SMEs): The rise of e-commerce has driven many SMEs to establish an online presence, boosting the demand for website builders. According to the U.S. Small Business Administration (SBA), as of 2023, 71% of small businesses had a website, up from 64% in 2021. This growth indicates a strong trend towards digital adoption among SMEs, fueling the Website Builders Market.Growing demand for mobile-responsive websites: With the increasing use of smartphones for internet browsing, there's a rising need for mobile-responsive websites. In 2023, 85% of Americans owned a smartphone, up from 81% in 2021. This trend has led to a surge in demand for website builders that offer mobile-responsive templates and designs.Shift towards no-code/low-code development platforms: The popularity of no-code and low-code development platforms has significantly contributed to the growth of the Website Builders Market.

Survey of 2,000 businesses on how much they spend on their website and their website costs

Website Builders Market is expected to surpass the value of USD 3.3 Billion by 2032, expanding at a CAGR of xx.x% during the forecast period.

Context

The cleaned text data can be used to adapt LLM to the domain of Norwegian Agriculture within the Norwegian language. In addition, it can be valuable for various NLP tasks such as region classification, or analytical tasks, such as exploring common agricultural practices in Norway.

Content

This dataset focuses on agronomic management practices and production in Norway. It consists of 2292 articles in Norwegian. All data is derived from three Norwegian agricultural-related websites and includes data from the largest advisory service for the agricultural sector, Norsk landbruksrådgivning (Norwegian Agricultural Extension Service, NLR), the most prominent agricultural research institute in Norway, Norsk Institutt for Bioøkonomi (Norwegian Institute for Bioeconomy, NIBIO), and the most comprehensive web page dedicated to plant protection in agriculture, Plantevernleksikonet.

Inspiration

The emergence of LLMs marked a significant step forward, providing a single solution for generating human-like text. However, training an LLM requires substantial amounts of text data, which is not readily available for most natural languages, including Norwegian. And agriculture as an industry has not seen much penetration of AI, - what if we could provide location-specific insights to a farmer?

Acknowledgements

The data from NLR can be expanded in the future, gathering more text data.

The global website monitoring services market size was valued at USD 25.21 billion in 2023 and is projected to reach USD 64.76 billion by 2033, exhibiting a CAGR of 9.2% during the forecast period. The market growth can be attributed to the increasing adoption of cloud-based services, the rising number of online businesses, and the growing need for ensuring website uptime and performance. The market is segmented based on type into on-premise, cloud-based, and application. The cloud-based segment held the largest share in 2023 and is expected to maintain its dominance throughout the forecast period. The growing popularity of cloud computing due to its cost-effectiveness and scalability is driving the growth of this segment. Additionally, the increasing demand for website performance monitoring in real-time is fueling the adoption of cloud-based website monitoring services. The application segment is further divided into SMEs, large enterprises, and others. The large enterprises segment accounted for the largest share in 2023 and is expected to continue its dominance in the coming years. The high dependency of large enterprises on websites for business operations and their ability to invest in website monitoring solutions are the key factors driving the growth of this segment.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.