As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.

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 January 2025, the ranking of the most popular YouTube channels based on monthly views is dominated by music and children's content. Wiz Khalifa's Music channel was ranked first with a whopping six billion channel views, while Wow Kidz ranked second with over five billion video views in the last examined month. Indian music record channel T-Series -which ranked first continuously in 2021 and 2022, placed third with 2.72 billion video views. Most subscribed YouTube channels When looking at the most-subscribed YouTube channels: Indian music label T-Series was ranked first with 229 million channel subscribers. The video game commentator Felix Kjellberg, aka PewDiePie, ranked seventh, with roughly 111 million subscribers, after being surpassed by Jimmy Donaldson, aka MrBeast in November 2022. As of June 2022, PewDiePie was still the most subscribed gaming-content channel on YouTube, followed by Salvadorian YouTuber Fernanfloo with 45 million global subscribers. Creators' earnings MrBeast was the highest-earning YouTuber in 2021, with 54 million U.S. dollars. Ryan Kaji from Ryan's World was among the youngest content creators making the rankings of the highest-earning YouTubers in 2021, with an estimated revenue of 27 million U.S. dollars. Ryan’s channel was set up by his parents in March 2015, when the young content creator was three years old. MrBeast was also the leading content creator on YouTube based on Influence Media Value, while PewDiePie ranked third with an evaluation of around 3.91 million U.S. dollars.

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

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...

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

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How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...

YouTube Video and Channel Analytics

YouTube Video and Channel Analytics: Statistics and Features

By VISHWANATH SESHAGIRI [source]

About this dataset

The YouTube Video and Channel Metadata dataset is a comprehensive collection of data related to YouTube videos and channels. It consists of various features and statistics that provide insights into the performance and engagement of videos, as well as the overall popularity and success of channels.

The dataset includes both direct features, such as total views, channel elapsed time, channel ID, video category ID, channel view count, likes per subscriber, dislikes per subscriber, comments per subscriber, and more. Additionally, there are indirect features derived from YouTube's API that provide additional metrics for analysis.

One important aspect covered in this dataset is the ratio between certain metrics. For example: - The totalviews/channelelapsedtime ratio represents the average number of views a video has received relative to the elapsed time since the channel was created. - The likes/dislikes ratio indicates the proportion of likes on a video compared to dislikes. - The views/subscribers ratio showcases how engaged subscribers are by measuring the number of views relative to the number of subscribers.

Other metrics explored in this dataset include comments/views ratio (representing viewer engagement), dislikes/views ratio (measuring viewer sentiment), comments/subscriber ratio (indicating community participation), likes/subscriber ratio (reflecting audience loyalty), dislikes/subscriber ratio (highlighting dissatisfaction levels), total number of subscribers for a channel (subscriberCount), total views on a channel (channelViewCount), total number of comments on a channel (channelCommentCount), among others.

By analyzing these features and statistics within this dataset, researchers or data analysts can gain valuable insights into various aspects related to YouTube videos and channels. Furthermore, it may be possible to build statistical relationships between videos based on their performance characteristics or even develop topic trees based on similarities between different content categories. This dataset serves as an excellent resource for studying YouTube's ecosystem comprehensively.

For accessing additional resources related to this dataset or exploring code repositories associated with it, users can refer to the provided GitHub repository

How to use the dataset

Introduction:

Step 1: Understanding the Dataset Start by familiarizing yourself with the columns in the dataset. Here are some key features to pay attention to:

• totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
• channelViewCount: The total number of views on the channel.
• likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
• views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
• subscriberCount: The total number of subscribers for a channel.
• dislikes/views: The ratio of dislikes on a video to its total views.
• comments/subscriber: The ratio comments on a video receive per subscriber count.

Step 2: Determining Data Analysis Objectives Define your objectives or research questions before diving into data analysis using this dataset. For example, you may want to explore relationships between viewership, engagement metrics, and various attributes such as category ID or elapsed time.

Step 3: Analyzing Relationships between Variables Use statistical techniques like correlation analysis or visualization tools like scatter plots, bar graphs, or heatmaps to understand relationships between variables in this dataset.

For example: - Plotting totalviews/channelelapsedtime against channelViewCount can help identify patterns between overall video popularity and channels' view count growth over time. - Comparing likes/dislikes with comments/views can give insights into viewer engagement levels across different videos.

Step 4: Building Machine Learning Models (Optional) If your objective includes predictive analysis or building machine learning models, select relevant features as predictors and the target variable (e.g., totalviews/channelelapsedtime) for training and evaluation.

You can use various algorithms such as linear regression, decision trees, or neural networks to predict video performance or channel growth based on available attributes.

Step 5: Evaluating Model Performance Assess the predictive model's performance using appropriate evaluation metrics like mean square...

As of January 2025, Thibaud Delapart Mazăre, known as Tibo InShape had the most popular YouTube channel in France, with almost 26 million subscribers. Second-ranked YouTuber Squeezie had approximately 19.3 million subscribers, while the popular music channel Lofi Girl ranked third, with 14.7 million subscribers. YouTube usage in France  As of July 2024, YouTube saw almost 50.2 million active YouTube users in France, reporting a reach of over 86 percent among the French digital population. In 2023, French YouTube users engaged with the platform to find music and video content, with the terms "musique" and "film" reporting the highest index value among all examined queries. Mobile video consumption   In 2023, French users were more likely to consume digital video content on their smartphones than on laptops or Smart TVs, a sign that mobile video is becoming an increasingly popular format in the country. In 2024, mobile users in France spent almost 17 hours per month on the video sharing platform. In the second quarter of 2022, the YouTube mobile app was downloaded over 600 thousand times by iOS users, indicating the continued popularity of the app.

List of top YouTubers and their estimated earnings and subscriber counts in 2024.

The most subscribed YouTube channels in the world are split evenly between brands and individual YouTubers. Many of the most successful company channels actually serve Indian audiences – such as T-Series, SET India, and Zee Music Company. All of these specialize in movies and music, often with Bollywood ties. One name even features twice on our Top 50 list: The main T-Series channel is the second-most subscribed YouTube channel in the world, at 300 million subs, while its devotional movies channel Bhakti Sagar ranks 27th. Among the 6 non-corporate YouTubers in the Top 10 are the overall leader MrBeast, three kids’ content creators – Kids Diana Show, Like Nastya, and Vlad and Niki – and two newer additions, the Stokes Twins and KIMPRO. They both recently passed by PewDiePie, who has now dropped out of the leaders of the pack. You won’t be surprised that the creator with the most subscribers on the platform is Jimmy Donaldson. After shooting past long-time leader T-Series and first cracking the magic 300 million in mid-2024, then 400 million in June 2025, MrBeast now holds at over 430 million. It's an outsize fanbase that earns him an outsize income. We’ve also already mentioned just how popular child YouTubers like Nastya or Diana are. Today, Vlad and Niki hold the #2 creator spot with 145 million subs, now pulling ahead of Kids Diana Show at 136 million (#3). Next the Stokes Twins, Alan and Alex, who rocketed up this entire list last year to become the #4 creators and the #7 top subscribed channel overall. The twin’s antics are now followed by a cool 131 million YouTube subscribers. Freshly overtaken, Like Nastya is not far behind at 130 million subscribers in spot #5. The #6 creator channel is the South Korean comedy skit channel 김프로KIMPRO, which features the eponymous Kim Pro and a variable cast of female creators. At 119 million subs, KIMPRO is already the #10 most subscribed channel in the world. It, too, has been growing rapidly, adding millions of fans every month. A few weeks ago, Kim overtook a former long-term subs leader, video game player PewDiePie AKA Felix Kjellberg (110 million, #7 creator). Kjellberg's channel updates only sporadically, though. It has leaked about a million followers over the last year. Rank #8 now belongs to Shorts star Alan Chikin Chow on Alan's Universe. Chow's comedic talent has earned him 97 million followers - more than triple what he had a year ago, when he was crowned (once more) the most famous Shorts YouTuber. On the list since the first days of 2025 is Instagram, TikTok and YouTube phenomenon Alejo Igoa, an Argentinian comedian filming silly stuff with his friends. At some 91 million subs, he's already the #9 creator on this list – and he's been climbing fast. Not far behind him comes the comedy skit channel A4, run by a team of Belarusian creators. They've amassed 89 million subscribers. After edging onto the top 50 list at the beginning of last year, A4 are now #20 overall, and the #10 creator channel. Next up is Indian family comedy channel KL BRO Biju Rithvik, which made it onto this list just a few months ago, now with around 77 million subscribers. This makes them the #11 creator channel. About to overtake them is Topper Guild - the TikTok personality whose first dubious claim to fame was wasting food for views, and who has now moved on to glitzy MrBeast-style clips. Having entered the list in March, he is already the #12 creator with some 76 million fans. This leaves ex-NASA and Apple engineer Mark Rober in spot #13. His eponymous DIY engineering and science channel is holding at 71 million subs – and continues to grow slowly, but steadily. About a million subs behind him is Uruguayan comedy-horror YouTuber Fede Vigevani. Listed here for a bit more than a year - since June 2024 - he today has nearly 70 million subscribers, which earns him the #14 creator spot. Additionally, he is the single most-watched Spanish-speaking creator (just ahead of YOLO - see below). The third newbie in 2025 is Japanese Short and TikTok creator ISSEI / いっせい, AKA issei0806, whose funny travelogues and parodies of popular content formats launched him onto this list only in June. Issei has already climbed to 67 million subs and to the #15 creator spot. 2025's fourth new entry are the guys from YOLO AVENTURAS, a Mexico-based pranks, comedy and MrBeast-style challenge channel around Venezuelan Flavio Andrés Broianigo Muñoz (aka YOLO) and his siblings. Their 65 million subscribers make them the #16 creator channel. And: We're optimistic that they'll stay with us. Pretty fresh on #17 with some 64 million subscribers comes Indian child sensation Anaya Kandhal. A month ago, she finally shouldered Dude Perfect out of the top 50 YouTubers - despite their big plans. Anaya's channel is also #50 overall, neatly capping off this list.

This dataset contains two files for analyzing the relationship between the popularity of a certain video and the most relevant/liked comments of said video.

File Descriptions

videos-stats.csv: This file contains some basic information about each video, such as the title, likes, views, keyword, and comment count.

comments.csv: For each video in videos-stats.csv, comments.csv contains the top ten most relevant comments as well as said comments' sentiments and likes.

Column Descriptions

videos-stats.csv: - Title: Video Title. - Video ID: The Video Identifier. - Published At: The date the video was published in YYYY-MM-DD. - Keyword: The keyword associated with the video. - Likes: The number of likes the video received. If this value is -1, the likes are not publicly visible. - Comments: The number of comments the video has. If this value is -1, the video creator has disabled comments. - Views: The number of views the video got.

comments.csv: - Video ID: The Video Identifier. - Comment: The comment text. - Likes: The number of likes the comment received. - Sentiment: The sentiment of the comment. A value of 0 represents a negative sentiment, while values of 1 or 2 represent neutral and positive sentiments respectively.

Applicability

• Sentiment Analysis with comments
• Text Generation with comments
• Predicting video likes from comment information
• Popularity Analysis by Keyword
• Popularity Analysis
• Prediction video views from comment information/video statistics
• In-depth EDA of the 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!

This paper investigates the profiles, challenges, and motivations of science communicators of the ScienceVlogs Brasil project—a Brazilian alliance of YouTube channels that disseminate science information on YouTube. It also looks into their prospects of professionalization in science communication and their strategies to improve their skills, increase public reach, and have financial support. Results show that the typical science YouTuber is a highly educated young male who works primarily in the education field, wishes to improve science understanding and fight misinformation, and is challenged by YouTube's highly competitive environment and restrictive algorithms. Science YouTubers also strive to improve their skills and techniques to produce high-quality, professional-like videos to become visible on the platform.

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 15 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 on the top spot for longest (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 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. As of 2022, about 40 percent of the U.S. digital music audience uses YouTube Music. 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 from release, making it the most viral movie trailer.

As of September 13, 2025, ******************* was the most-subscribed YouTube channel in Hong Kong, attracting about **** million subscribers worldwide. Other most popular YouTube channels in Hong Kong were ******************** and *****************.

Context

As YouTube becomes one of the most popular video-sharing platforms, YouTuber is developed as a new type of career in recent decades. YouTubers earn money through advertising revenue from YouTube videos, sponsorships from companies, merchandise sales, and donations from their fans. In order to maintain a stable income, the popularity of videos become the top priority for YouTubers. Meanwhile, some of our friends are YouTubers or channel owners in other video-sharing platforms. This raises our interest in predicting the performance of the video. If creators can have a preliminary prediction and understanding on their videos’ performance, they may adjust their video to gain the most attention from the public.

You have been provided details on videos along with some features as well. Can you accurately predict the number of likes for each video using the set of input variables?

Content

Train Set

video_id -> Identifier for each video

title -> Name of the Video on Youtube

channel_title -> Name of the Channel on Youtube

category_id -> Category of the Video (anonymous)

publish_date -> The date video was published

tags -> Different tags for the video

views -> Number of views received by the Video

dislikes -> Number of dislikes on the Video

comment_count -> Number on comments on the Video

description -> Textual description of the Video

country_code -> Country from which the Video was published

likes -> Number of Likes on the video

Acknowledgements

Thank You Analytics Vidhya for providing this dataset.

In 2024, YouTube channels counting over ****** followers saw their video views decrease to *** per video, down by ** percent year-over-year. In comparison, small accounts with between *** and ***** followers saw approximately **** views per video in 2024, down compared to the **** views recorded in 2023.

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.

Seen the popularity of the YouTube platform among younger users and children, kids-themed YouTube channels are among the most popular types of content on the platform. As of March 2024, ChuChu TV Nursery Rhymes & Kids Songs was the most subscribed kids' content channel, with approximately 94.7 million subscribers. Spanish-language channel El Reino Infantil ranked second with approximately 67.5 million subscribers, while Masha and the Bear ranked third with 53.3 million subscribers. Kids on YouTube According to a survey of global parents conducted in September 2022, over half of the respondents reported that watching YouTube videos was a popular online activity their children chose to engage with. In the United States, children were using the YouTube app to watch content for over one hour daily on average in 2023. Children media consumption As of May 2022, software, audio, and video content were the most popular categories for global children to interact with online, with approximately 44 percent of younger users visiting and engaging with these content formats. Between 52 percent and 55 percent of children in India, Saudi Arabia, and South Africa were reported visiting software, audio, and video websites between 2020 and 2021.

📺 YouTube-Commons 📺

YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license.

  Content

The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance information… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/YouTube-Commons.