On June 17, 2016, Korean education brand Pinkfong released their video "Baby Shark Dance", and the rest is history. In January 2021, Baby Shark Dance became the first YouTube video to surpass 10 billion views, after snatching the crown of most-viewed YouTube video of all time from the former record holder "Despacito" one year before. "Baby Shark Dance" currently has over 16 billion lifetime views on YouTube. Music videos on YouTube “Baby Shark Dance” might be the current record-holder in terms of total views, but Korean artist Psy’s “Gangnam Style” video remained in the top spot for the longest time (1,689 days, or 4.6 years) before ceding its spot to its successor. With figures like these, it comes as little surprise that the majority of the most popular videos on YouTube are music videos. Since 2010, all but one of the most-viewed videos on YouTube have been music videos, signifying the platform’s shift in focus from funny, viral videos to professionally produced content. Popular video content on YouTube Music fans are also highly engaged audiences, and it is not uncommon for music videos to garner significant amounts of traffic within the first 24 hours of release. Other popular types of videos that generate lots of views after their first release are movie trailers, especially superhero movies related to the MCU (Marvel Cinematic Universe). The first official trailer for the upcoming film “Avengers: Endgame” generated 289 million views within the first 24 hours of release, while the movie trailer for Spider-Man: No Way Home generated over 355 views on the first day of release, making it the most viral movie trailer.

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.

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

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"]

This dataset is a daily record of the top trending YouTube videos and it will be updated daily.

Context

YouTube maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”.

Note that this dataset is a structurally improved version of this dataset.

Content

This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the IN, US, GB, DE, CA, FR, RU, BR, MX, KR, and JP regions (India, USA, Great Britain, Germany, Canada, France, Russia, Brazil, Mexico, South Korea, and, Japan respectively), with up to 200 listed trending videos per day.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the 11 regions in the dataset.

For more information on specific columns in the dataset refer to the column metadata.

Acknowledgements

This dataset was collected using the YouTube API. This dataset is the updated version of Trending YouTube Video Statistics.

Inspiration

Possible uses for this dataset could include: - Sentiment analysis in a variety of forms - Categorizing YouTube videos based on their comments and statistics. - Training ML algorithms like RNNs to generate their own YouTube comments. - Analyzing what factors affect how popular a YouTube video will be. - Statistical analysis over time.

For further inspiration, see the kernels on this dataset!

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

As of July 2022, the largest bulk of YouTube video views in the United States was continued for over ** percent by influencer-published videos. Videos created by media companies generated approximately *** percent of all the views on the popular social video platform, while videos created by brands or aggregators generated only *** percent of all the views on YouTube as of the examined period.

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.

Description:

This dataset contains statistics for a selection of YouTube videos, capturing metrics such as views, comments, likes, dislikes, and the timestamp when the data was recorded. The dataset provides insights into the popularity and engagement levels of these videos as of April 15, 2019. This data can be useful for analyzing trends in video performance, user engagement, and the impact of content over time.

File Description: This CSV file contains detailed statistics for a set of YouTube videos, including unique video identifiers and various engagement metrics. Each row represents a different video, and the columns provide specific data points related to the video's performance.

Column Descriptions

videostatsid: Unique identifier for each video statistics entry. ytvideoid: Unique YouTube video identifier. views: The total number of views the video has received. comments: The total number of comments posted on the video. likes: The total number of likes the video has received. dislikes: The total number of dislikes the video has received. timestamp:The date and time when the statistics were recorded, in the format YYYY-MM-DD HH:MM

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.

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.

Introduction

YouTube Creator Statistics: The overall Creator Economy rose to fame since the COVID-19 pandemic era, 4 years ago. When every other person was stuck in their home and scrolling through social media. It is expected that by 2027, this sector will reach $480 billion, and it has no signs of slowing down. Using the internet, many people are creating a source of income by making videos and uploading them to video content platforms, such as TikTok, Moj, YouTube, Instagram, Facebook, etc.

YouTube is one of the video content platforms. It was launched on 14 February 2005 by Steve Chen, Chad Hurley, and Jawed Karim. YouTube helped content creators around the world earn passive income by making videos and uploading them on the platform. For some creators, YouTube acts as the primary source of income. You’ll understand more about YouTube Creator Statistics by the data provided below.

As of June 2022, more than *** hours of video were uploaded to YouTube every minute. This equates to approximately ****** hours of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumer’s appetites for online video has grown. In fact, the number of video content hours uploaded every 60 seconds grew by around ** percent between 2014 and 2020. YouTube global users Online video is one of the most popular digital activities worldwide, with ** percent of internet users worldwide watching more than ** hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately *** million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than ** billion U.S. dollars. YouTube video content consumption The most viewed YouTube channels of all time have racked up billions of viewers, millions of subscribers and cover a wide variety of topics ranging from music to cosmetics. The YouTube channel owner with the most video views is Indian music label T-Series, which counted ****** billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.

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.

According to a survey conducted in Indonesia in *********, more than ** percent of male and female respondents tended to giving likes or dislikes when they saw Youtube videos posted by the Youtuber they subscribed to. Indonesia is one of the fastest rising e-commerce countries in the the world, with an expected number of e-commerce user of about ***** millions by 2023.

Descriptive statistics of network structure by video category.

From new YouTube channels in Asia, videos of the film & animation category received the highest average views per video in the first 11 months of 2021, at over *** thousand views. Music was the second most viewed category per video, at over *** thousand views on average.

As of January 2025, T-Series had reached surpassed 281 billion lifetime video views, making it the most-viewed YouTube channel owner of all time. Popular children's rhymes and song channel Cocomelon followed, with 194 billion lifetime views. Several children-themed channels such as Like Nastya, Kids Diana Show, and Vlad and Niki appeared in the ranking. YouTube is one of the most beloved platforms for children and teens In recent years, video content not only has become one of the most popular types of online formats for children but also one of the highest-performing online categories. The most viewed video on YouTube has been for several years "Baby Shark Dance," published by Korean education brand Pinkfong in June 2016. The catchy rhyme was the first YouTube video to reach and surpass 10 billion views, and as of January 2024 was sitting at 13.93 billion global views. YouTube channels that propose content for children and young teens are also among the most subscribed channels on the platform, with 3D-animation channel Cocomelon counting almost 188 million subscribers worldwide as of January 2025. Children mobile video app usage According to a survey of global parents conducted in September 2021, watching YouTube videos was the most common mobile activity for 54 percent of children, second only to online gaming. Additionally, watching movies and watching content on social media were indicated by 30 percent and 20 percent of parents as usual activities for their children to engage with on mobile devices. In the United States and in the United Kingdom, YouTube and Netflix were the most popular video apps for children to engage with.

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.

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.