Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

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.

From April 2022 to January 2024, the website for mobile language learning app Duolingo saw an average of approximately 89 million visits, ranking as the most popular education site worldwide. Coursera, which offers classes and certifications on various subjects, saw 60 million visits from global users in the examined period. Khan Academy, a U.S.-based non-profit platform for students and teachers, ranked third with 36.59 million visits recorded on average between April 2022 and the beginning of 2024.

As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

Digital technology and Internet use, website traffic strategies, by North American Industry Classification System (NAICS) and size of enterprise for Canada from 2012 to 2013.

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

According to a report published by DataReportal, as of November 2023, the most visited website in Thailand was Google.com with approximately 885 million monthly visits. This was followed by Youtube.com with around 456 million monthly visits in that year.

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This dataset provides the number of users to the Valuation Office website.

Introduction

Twilio Statistics: Twilio Inc. is a cloud communications company headquartered in San Francisco, California. As of March 31, 2025, Twilio reported a revenue of $1.17 billion for the first quarter, marking a 12% year-over-year increase. The company had over 335,000 active customer accounts and a dollar-based net expansion rate of 107%. Twilio employed 5,535 individuals at the end of 2024. In the first quarter of 2025, Twilio achieved a GAAP net income of $20 million and a non-GAAP net income of $184 million. The company generated $191 million in net cash from operating activities and $178 million in free cash flow during the same period.

Looking ahead, Twilio raised its full-year 2025 organic revenue growth guidance to a range of 7.5% to 8.5% and its non-GAAP income from operations and free cash flow targets to between $850 million and $875 million.

This article delves deeper into the 2024 key Twilio statistics, giving a concise yet clear picture of its financial results, customer metrics, and strategic undertakings.

Introduction

XUMO Statistics: XUMOÂ is a free, ad-supported streaming service that experienced significant growth in 2024. The platform reached 40 million monthly active users, marking a substantial increase from previous years. Xumo's content library expanded to include over 190 live channels and more than 1,000 movies, offering a diverse range of entertainment options. The service is available in multiple countries, including the United States, Canada, France, Germany, the United Kingdom, Brazil, Mexico, and Italy.

In recent years, Xumo has seen a 300% growth in revenue, highlighting its increasing popularity in the FAST (Free Ad-Supported TV) market. The platform is accessible on various devices, such as smart TVs, mobile phones, and desktops, making it convenient for users to stream content anytime, anywhere.

This article explores key statistics that show how XUMO has performed and what factors influenced its success in 2024.

Introduction

Buffer vs Hootsuite Statistics: Buffer and Hootsuite are working against each other for supremacy in scheduling, analytics, collaboration, and affordability. Buffer offers simple interfaces and transparent pricing for creators and small and medium enterprises. Hootsuite markets to larger enterprises for deep analytics, bulk management tools, and integrations comprising its internal operations.

This comprehensive Buffer vs Hootsuite statistics focuses on user growth, satisfaction, pricing, features, and ease of use, all backed by numbers and insights from various sources. So, by the end, you will have an informative and data-driven sense of what platform will suit your needs better.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

A complete list of live websites using the Visitors Traffic Real Time Statistics technology, compiled through global website indexing conducted by WebTechSurvey.

As of 2019, direct traffic accounts for the largest percentage of website traffic worldwide, with a share of 55 percent. Additionally, search traffic accounts for 29 percent of worldwide website traffic.

Unique visitors, total sessions, and bounce rate for lacity.org, the main website for the City of Los Angeles.

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

Introduction

Bluehost vs SiteGround Statistics: Bluehost and SiteGround are two commonly used web hosting providers, which are crucial for building a fast, secure, and reliable website. Bluehost is a web hosting and domain registration company that sells shared hosting, WordPress hosting, VPS hosting, dedicated hosting, and WooCommerce hosting, as well as professional marketing services. In contrast, SiteGround is a web hosting company that provides shared hosting, cloud hosting, enterprise solutions, email hosting, and domain registration.

This article presents several statistical analyses from various perspectives, including market analysis, unique features, user base, usage, pricing, speed, scalability, and backend technology. Go through the overall analysis; you will gain a clear idea of how to choose a better option based on users' needs.