As of October 2025, India was the country with the largest YouTube audience by far, with approximately 500 million users engaging with the popular social video platform. The United States followed, with 254 million YouTube viewers. Indonesia came in third, with 151 million users watching content on YouTube. The United Kingdom saw 55.5 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? Saudi Arabia was the country with the highest YouTube penetration worldwide, as nearly 96 percent of the country's digital population engaged with the service. In 2025, YouTube counted 125 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets YouTube is among the most popular social media platforms worldwide. In terms of in-app (IAP) revenue, the YouTube app generated approximately 53 million U.S. dollars in the United States in December 2024, as well as 17 million U.S. dollars in Japan.

Key YouTube Statistics (Editor’s Choice) YouTube recorded 70 billion monthly active users in March 2023, which includes 55.10% of worldwide active social media users. There have been more than 14 million daily active users currently on YouTube, in the United States of America this platform is accessed by 62% of users. YouTube is touted as the second largest search engine and the second most visited website after Google. Revenue earned by YouTube in the first two quarters of 2023 is around $14.358 billion. In 2023, YouTube Premium and YouTube Music have recorded 80 million subscribers collectively worldwide. YouTube consumers view more than a billion hours of video per day. YouTube has more than 38 million active channels. In the fourth quarter of 2021, YouTube ad revenue has been $8.6 billion. Around 3 million paid subscribers to access YouTube TV. YouTube Premium has around 1 billion paid users. In 2023, YouTube was banned in countries such as China excluding Macau and Hong Kong, Eritrea, Iran, North Korea, Turkmenistan, and South Sudan. With 166 million downloads, the YouTube app has become the second most downloaded entertainment application across the world after Netflix. With 91 million downloads, YouTube Kids has become the sixth most downloaded entertainment app in the world. Nearly 90% of digital consumers access YouTube in the US, making it the most popular social network for watching video content. Over 70% of YouTube viewership takes place on its mobile application. More than 70% of YouTube video content watched by people is suggested by its algorithm. The average duration of a video on YouTube is 12 minutes. An average YouTube user spends 20 minutes and 23 seconds on the platform daily. Around 28% of YouTube videos that are published by popular channels are in the English language. 77% of YouTube users watch comedy content on the platform. With 247 million subscribers, T-Series has become the most subscribed channel on YouTube. Around 50 million users log on to YouTube every day. YouTube's biggest concurrent views record has been at 2.3 billion from when SpaceX has gone live on the platform to unveil Falcon Heavy Rocket. The majority of YouTube users are in the age group of 15 to 35 years in the US. The male-female ratio of YouTube users is 11:9. Apple INC. has been touted as the biggest advertiser on YouTube in 2020 spending $237.15 million. YouTube produced total revenue of $19.7 billion in 2020. As of 2021, the majority of YouTube users (467 million) are from India. It is the most popular platform in the United States with 74 percent of adult users. YouTube contributes to nearly 25% of mobile traffic worldwide. Daily live streaming on YouTube has increased by 45% in total in 2020. In India, around 225 million people are active on the platform each hour as per the 2021 statistics. YouTube Usage and Viewership Statistics #1. YouTube accounts for more than 2 billion monthly active users Around 2.7 billion users log on to YouTube each month. The number of monthly active users of YouTube is expected to grow even further. #2. Around 14.3 billion people visit the platform every month The number of YouTube visitors is far higher compared to Facebook, Amazon, and Instagram. #3. YouTube is accessible across 100 countries in 80 languages. The platform is widely available across different communities and nations. #4. 53.9% of YouTube users are men and 46.1% of women use the platform As of 2023 statistics, 53.9% of men use the platform and 46.1% of women over 18 years are on YouTube. The share in the number of males and females is 1.38 billion and 1.18 billion respectively. Age Group Male Female 18 to 24 8.5% 6% 25 to 34 11.6% 8.6% 35 to 44 9% 7.5% 45 to 54 6.2% 5.7% 55 to 64 4.4% 4.5% Above 65 4.3% 5.4% #5. 99% of YouTube users are active on other social media networks as well. Fewer than 1% of YouTube users are solely dependent on the platform. #6. Users spend around 20 minutes and 23 seconds per day on YouTube on average It is quite a generous amount of time spent on any social network platform. #7. YouTube is the second most visited site worldwide With more than 14 billion visits per month, YouTube has become the second most visited site in the world. However, its parent company Google is the most visited site across the globe. As per the statistics, YouTube is the third most popular searched word on Google. #8. 694000 hours of video content are streamed on YouTube per minute YouTube has outweighed Netflix as well in terms of streaming video content. #9. Over 81% of total internet users have accessed YouTube #10. Nearly 450 million hours of video content are uploaded on YouTube each hour More than 5 billion videos are watched on YouTube per day. #11. India has the maximum numb

In 2023, all the analyzed channels with an audience between 50,000 and 55 million subscribers had over 418,000 disliked on YouTube, against the approximately 17 million likes recorded in 2023. In comparison, all the tiny accounts analyzed - which had up to 500 subscribers - managed to accumulate a total of one million likes, as well as 53,600 dislikes and 41,430 comments.

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

As of February 2025, 12 percent of the YouTube global audience was composed of male users aged between 25 and 34 years, as well as around 9.7 percent of female users of the same age. Male users aged between 35 and 44 years on the platform accounted for 10.1 percent of the total, while women of the same age using YouTube had an audience share of 8.4 percent in the examined period. YouTube’s global popularity The number of monthly active users on YouTube reached almost 2.5 billion in April 2024, making it the second most popular social network on the internet. The platform's popularity spans all over the world, with India and the United States having the largest YouTube audiences. As of April 2024, the audience of YouTube in India was around 476 million, while the United States recorded a YouTube audience of around 238 million users.

YouTube’s digital revenues One of YouTube's leading monetization methods include advertising, with the company generating around 8.09 billion U.S. dollars in the first quarter of 2024. Additionally, the platform generated over 28 million dollars in the United States through in-app purchases, as well as over 19.2 million U.S. dollars in revenues from mobile app users in Japan.

About the Dataset : Youtube Views Prediction

This dataset contains information about trending YouTube videos from multiple countries, providing valuable insights for predicting video popularity based on various attributes. The dataset includes both numerical and categorical features that are essential for analyzing viewer behavior, engagement, and trends in content creation. The original source of this dataset can be found at : https://www.kaggle.com/datasets/datasnaek/youtube-new/data

Columns and Their Descriptions:

title: The title of the YouTube video.
channel_title: Name of the channel that published the video.
trending_date: The date the video started trending.
publish_date: The original upload date of the video.
publish_time: The exact time the video was published.
views: The total number of views the video received.
likes: The number of likes the video received.
dislikes: The number of dislikes the video received.
comment_count: The total number of comments on the video.
tags: Keywords or tags associated with the video, helping discoverability.
description: A detailed text description provided by the uploader.
category_id: The category assigned to the video (e.g., Music, Gaming, News).

Business Goals :

Predicting the number of views on youtube videos based on video attributes. The goal is to develop a model that can accurately predict the number of views a video will receive, using various video attributes such as likes, shares, comments, video duration, and more.

Business Metrics :

1. RMSE (Root Mean Squared Error) RMSE is a metric that measures the magnitude of the error between the values predicted by the model (Predicted Views) and the actual values (Actual Views). The lower the RMSE value, the more accurate the model's predictions.

2. R² (Coefficient of Determination) R² measures the extent to which the model can explain the variation in the data. R² values range from 0 to 1, where 1 means the model can explain all the variation in the number of views based on the given attributes, and 0 means the model cannot explain the variation. The higher the R², the better the model is at predicting views and the more relevant the features used in the model.

Model & Evaluation

The machine learning model was evaluated using several approaches, including different pre-processing techniques and multiple ML models. Ultimately, the chosen model for this analysis is the Random Forest Regressor. The final evaluation results show an RMSE of 630.741, indicating an average prediction error of approximately 630.741 units. Additionally, the R² score is 0.9623, meaning that the model explains 96.23% of the variance in the data (number of views). These results were deemed satisfactory and were selected as the final modeling approach for the system and its potential future applications.

📢 Published on : My LinkedIn

Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.

As of February 2025, approximately 54 percent of YouTube users were male. By comparison, female users on the popular social video platform were approximately 46 percent of the total. In the last examined period, the United Arab Emirates and Israel were among the country with the highest YouTube penetration worldwide.

In 2024, users engaged more with the videos they watched on YouTube compared to the previous year. The number of average interactions on YouTube grew to 2.36 in the last measured year. This is an increase compared to 2023, when the number of comments, likes, and share on pieces of content hosted on YouTube was of approximately 2.1 interactions on average.

During the first quarter of 2024, Huge YouTube accounts, which had over 50,000 followers, reported an engagement rate of approximately 6.2 percent on their short-format content. In comparison, engagement was sensibly lower on long-format videos, which reported an engagement rate of 1.72 percent for Huge accounts. Medium YouTube accounts, which had a following between 2,001 and 10,000 users, reported engagement ratings of almost three percent on their Shorts, while long videos had an engagement of around 0.15 percent.

This dataset explores YouTube channel information. It includes:

Channel Names Brand Focus (Gaming, Music, etc.) Subscriber Base (Millions) Primary Languages Content Categories (Broad & Specific) Country of Origin This data allows you to:

Analyze trends by language, category, and region. Identify popular content areas in various locations. Benchmark channel performance. Understand viewer demographics for different content types. By leveraging this dataset, you can gain valuable insights into YouTube's content landscape, audience makeup, and evolving trends.

Motivation

Study how YouTube videos become viral or, more in general, how they evolve in terms of views, likes and subscriptions is a topic of interest in many disciplines. With this dataset you can study such phenomena, with statistics about 1 million YouTube videos. The information was collected in 2013 when YouTube was exposing the data publicly: they removed this functionality in the years and now it's possible to have such statistics only to the owner of the video. This makes this dataset unique.

Context

This Dataset has been generated with YOUStatAnalyzer, a tool developed by myself (Mattia Zeni) when I was working for CREATE-NET (www.create-net.org) within the framework of the CONGAS FP7 project (http://www.congas-project.eu). For the project we needed to collect and analyse the dynamics of YouTube videos popularity. The dataset contains statistics of more than 1 million Youtube videos, chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

The motivation that led us to the development of the YOUStatAnalyser data collection tool and the creation of this dataset is that there's an active research community working on the interplay among user individual preferences, social dynamics, advertising mechanisms and a common problem is the lack of open large-scale datasets. At the same time, no tool was present at that time. Today, YouTube removed the possibility to visualize these data on each video's page, making this dataset unique.

When using our dataset for research purposes, please cite it as:

@INPROCEEDINGS{YOUStatAnalyzer, author={Mattia Zeni and Daniele Miorandi and Francesco {De Pellegrini}}, title = {{YOUStatAnalyzer}: a Tool for Analysing the Dynamics of {YouTube} Content Popularity}, booktitle = {Proc.\ 7th International Conference on Performance Evaluation Methodologies and Tools (Valuetools, Torino, Italy, December 2013)}, address = {Torino, Italy}, year = {2013} }

Content

The dataset contains statistics and metadata of 1 million YouTube videos, collected in 2013. The videos have been chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

Dataset structure

The structure of a dataset is the following: { u'_id': u'9eToPjUnwmU', u'title': u'Traitor Compilation # 1 (Trouble ...', u'description': u'A traitor compilation by one are ...', u'category': u'Games', u'commentsNumber': u'6', u'publishedDate': u'2012-10-09T23:42:12.000Z', u'author': u'ServilityGaming', u'duration': u'208', u'type': u'video/3gpp', u'relatedVideos': [u'acjHy7oPmls', u'EhW2LbCjm7c', u'UUKigFAQLMA', ...], u'accessControl': { u'comment': {u'permission': u'allowed'}, u'list': {u'permission': u'allowed'}, u'videoRespond': {u'permission': u'moderated'}, u'rate': {u'permission': u'allowed'}, u'syndicate': {u'permission': u'allowed'}, u'embed': {u'permission': u'allowed'}, u'commentVote': {u'permission': u'allowed'}, u'autoPlay': {u'permission': u'allowed'} }, u'views': { u'cumulative': { u'data': [15.0, 25.0, 26.0, 26.0, ...] }, u'daily': { u'data': [15.0, 10.0, 1.0, 0.0, ..] } }, u'shares': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [0.0, 0.0, 0.0, 0.0, ...] } }, u'watchtime': { u'cumulative': { u'data': [22.5666666667, 36.5166666667, 36.7, 36.7, ...] }, u'daily': { u'data': [22.5666666667, 13.95, 0.166666666667, 0.0, ...] } }, u'subscribers': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [-1.0, 0.0, 0.0, 0.0, ...] } }, u'day': { u'data': [1349740800000.0, 1349827200000.0, 1349913600000.0, 1350000000000.0, ...] } }

From the structure above is possible to see which fields an entry in the dataset has. It is possible to divide them into 2 sections:

1) Video Information.

_id -> Corresponding to the video ID and to the unique identifier of an entry in the database. title -> Te video's title. description -> The video's description. category -> The YouTube category the video is inserted in. commentsNumber -> The number of comments posted by users. publishedDate -> The date the video has been published. author -> The author of the video. duration -> The video duration in seconds. type -> The encoding type of the video. relatedVideos -> A list of related videos. accessControl -> A list of access policies for different aspects related to the video.

2) Video Statistics.

Each video can have 4 different statistics variables: views, shares, subscribers and watchtime. Recent videos have all of them while older video can have only the 'views' variable. Each variable has 2 dimensions, daily and cumulative.

views -> number of views collected by the video. shares -> number of sharing operations performed by users. watchtime -> the time spent by users watching the video, in minute. subscribers -> number of subscriptions to the channel the video is inserted in, caused by the selected video. day -> a list of days indicating the analysed period for the statistic.

Data usage

In the case you are using mongoDB as database system, you can import our dataset using the command:

mongoimport --db [MONGODB_NAME] --collection [MONGODB_COLLECTION] --file dataset.json

Once you imported the Dataset in your DB, you can access the data performing queries. Let's present some example code in python in order to perform queries.

The following code will perform a query without research parameters, returning all the entries in the database, each one saved into the variable entry:

client = MongoClient('localhost', 27017) db = client[MONGODB_NAME] collection = db[MONGODB_COLLECTION] for entry in db.collection.find(): print entry["day"]["data"]

If you want to restrict the results to some entries that answer to a specified query you can use:

client = MongoClient('localhost', 27017) db = client[MONGODB_NAME] collection = db[MONGODB_COLLECTION] for entry in (db.collection.find({"watchtime":{ "$exists": True }})) and (db.collection.find({"category":"Music"})): print entry["day"]["data"]

Between January and December 2024, "song" was the most searched keyword on YouTube by users worldwide, with an index rating of 100. The search query "movie" followed, with an index ranking of 63 relative points compared to the top-ranked result. Additionally, global online users were also interested in looking for online videos of DJs, with the query being indexed at 23 points.

rank: Position of the YouTube channel based on the number of subscribers Youtuber: Name of the YouTube channel subscribers: Number of subscribers to the channel video views: Total views across all videos on the channel category: Category or niche of the channel Title: Title of the YouTube channel uploads: Total number of videos uploaded on the channel Country: Country where the YouTube channel originates Abbreviation: Abbreviation of the country channel_type: Type of the YouTube channel (e.g., individual, brand) video_views_rank: Ranking of the channel based on total video views country_rank: Ranking of the channel based on the number of subscribers within its country channel_type_rank: Ranking of the channel based on its type (individual or brand) video_views_for_the_last_30_days: Total video views in the last 30 days lowest_monthly_earnings: Lowest estimated monthly earnings from the channel highest_monthly_earnings: Highest estimated monthly earnings from the channel lowest_yearly_earnings: Lowest estimated yearly earnings from the channel highest_yearly_earnings: Highest estimated yearly earnings from the channel subscribers_for_last_30_days: Number of new subscribers gained in the last 30 days created_year: Year when the YouTube channel was created created_month: Month when the YouTube channel was created created_date: Exact date of the YouTube channel's creation Gross tertiary education enrollment (%): Percentage of the population enrolled in tertiary education in the country Population: Total population of the country Unemployment rate: Unemployment rate in the country Urban_population: Percentage of the population living in urban areas Latitude: Latitude coordinate of the country's location Longitude: Longitude coordinate of the country's location

Potential Use Cases

YouTube Analytics: Gain valuable insights into the success factors of top YouTube channels and understand what sets them apart from the rest. Content Strategy: Discover the most popular categories and upload frequencies that resonate with audiences. Regional Influencers: Identify influential YouTube creators from different countries and analyze their impact on a global scale. Earnings Analysis: Explore the correlation between channel performance and estimated earnings. Geospatial Visualization: Visualize the distribution of successful YouTube channels on a world map and uncover geographical trends. Trending Topics: Investigate how certain categories gain popularity over time and correlate with world events.

During the fourth quarter of 2024, YouTube removed around 4.82 million channels from its popular video-sharing platform. This represents a slight decrease from the previous quarter, when the channels removed were approximately 4.87 million. YouTube channels are removed from the platform after three Community Guideline offenses or a single serious offense to the platform's guidelines.

Introduction

YouTube Shorts statistics: YouTube Shorts is currently everywhere, and if you are genuinely committed to expanding your reach in 2025, it is essential that you do not overlook it. Billions of views are generated each day, creators are achieving rapid success, and brands are eagerly participating to connect with new audiences.

YouTube Shorts has swiftly established itself as a primary platform for short videos, transforming the way creators present their content and interact with their audience. As an increasing number of individuals seek brief, engaging content, YouTube Shorts is not only influencing new viewing patterns but also providing opportunities for creators to expand their reach and generate income.

At present, the platform has approximately 2 billion monthly active users and around 70 million daily active users. Especially, a majority of Shorts viewers are male, with nearly 40% of them aged between 25 and 44.

Overview

This dataset contains engagement analytics from two prominent tech YouTube channels:

• WsCube Tech: A Hindi-language technology education channel covering web development, programming, and digital marketing.
• Google Developers: The official YouTube channel for Google’s developer ecosystem, featuring tech talks, product updates, and developer events.

Purpose

The purpose of this dataset is to analyze and compare the performance, engagement, and growth trends of both channels using metrics such as:

• VideoID
• Title
• UploadDate
• Views
• Likes
• Dislikes (Note: Not available via API since 2021)
• Comments

Possible Analysis

• 📈 Views Over Time: Growth and consistency of each channel
• 📊 Top Performing Videos: Views per video
• 🥧 Engagement Ratio: Pie chart showing likes vs comments distribution
• 📉 Upload Frequency Impact: How upload consistency affects views

Sources

Data collected using the YouTube Data API v3 between July 25–28, 2025. Only public video data is included.

Columns

Licensing

Data is collected from publicly available sources (YouTube API). No copyrighted content is included.

Suggested Tags

• youtube
• YtDataSet
• Dashboard
• data-visualization
• wsCubeTech
• google-developers
• engagement-metrics
• social-media-analytics

As of November 2024, South Korea saw the largest number of monthly mobile YouTube app opens. Android users in the country generated around 335.7 YouTube app opens on a monthly basis. Users in India followed, with 251.9 YouTube app opens, while Android users in Thailand saw 242.7 YouTube app opens in the examined period.

Use our YouTube Videos dataset to extract detailed information from public videos and filter by video title, views, upload date, or likes. Data points include video URL, title, description, thumbnail, upload date, view count, like count, comment count, tags, and more. You can purchase the entire dataset or a customized subset, tailored to your needs. Popular use cases for this dataset include trend analysis, content performance tracking, brand monitoring, and influencer campaign optimization.

This dataset contains data related to most watched YouTube videos till April 2024 . This contains different columns namely views,artist,channel,etc. The data is ranked on the basis of number of views.