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.

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.

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.

This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached. This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:

Track: name of the song, as visible on the Spotify platform. Artist: name of the artist. Url_spotify: the Url of the artist. Album: the album in wich the song is contained on Spotify. Album_type: indicates if the song is relesead on Spotify as a single or contained in an album. Uri: a spotify link used to find the song through the API. Danceability: describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. Energy: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. Key: the key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. Loudness: the overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. Acousticness: a confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. Instrumentalness: predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. Liveness: detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. Valence: a measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). Tempo: the overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. Duration_ms: the duration of the track in milliseconds. Stream: number of streams of the song on Spotify. Url_youtube: url of the video linked to the song on Youtube, if it have any. Title: title of the videoclip on youtube. Channel: name of the channel that have published the video. Views: number of views. Likes: number of likes. Comments: number of comments. Description: description of the video on Youtube. Licensed: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner. official_video: boolean value that indicates if the video found is the official video of the song. The data was last updated on February 7, 2023.

In February 2025, Alphabet was the most popular online video property in the United States, having accumulated over 255 million unique video viewers during the measured period. Comcast NBCUniversal ranked second with 224 million video viewers during the measured month.

YouTube was created in 2005, with the first video – Me at the Zoo - being uploaded on 23 April 2005. Since then, 1.3 billion people have set up YouTube accounts. In 2018, people watch nearly 5 billion videos each day. People upload 300 hours of video to the site every minute.

According to 2016 research undertaken by Pexeso, music only accounts for 4.3% of YouTube’s content. Yet it makes 11% of the views. Clearly, an awful lot of people watch a comparatively small number of music videos. It should be no surprise, therefore, that the most watched videos of all time on YouTube are predominantly music videos.

On August 13, BTS became the most-viewed artist in YouTube history, accumulating over 26.7 billion views across all their official channels. This count includes all music videos and dance practice videos.

Justin Bieber and Ed Sheeran now hold the records for second and third-highest views, with over 26 billion views each.

Currently, BTS’s most viewed videos are their music videos for “**Boy With Luv**,” “**Dynamite**,” and “**DNA**,” which all have over 1.4 billion views.

Headers of the Dataset Total = Total views (in millions) across all official channels Avg = Current daily average of all videos combined 100M = Number of videos with more than 100 million views

192 YouTube Channel Views Count

This project compiles and analyzes data from 192 YouTube channels, totaling approximately 166,411 videos. The dataset includes information such as video titles, view counts, publish dates, and authors.

  Introduction

The 192 YouTube Channel Views Count project aims to provide insights and analytics on video performance across 192 different YouTube channels. By aggregating data such as video titles, view counts, publish dates, and authors… See the full description on the dataset page: https://huggingface.co/datasets/leodeveloper2000/192-Youtube-Channel-Views-Count.

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.

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 Music Hits Dataset

A collection of YouTube music video data sourced from Wikidata, focusing on videos with significant viewership metrics.

  Dataset Description





  Overview

24,329 music videos View range: 1M to 5.5B views Temporal range: 1977-2024

  Features

youtubeId: YouTube video identifier itemLabel: Video/song title performerLabel: Artist/band name youtubeViews: View count year: Release year genreLabel: Musical genre(s)

  View… See the full description on the dataset page: https://huggingface.co/datasets/akbargherbal/youtube-music-hits.

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!

Our dataset offers a unique blend of attributes from YouTube and Google Maps, empowering users with comprehensive insights into online content and geographical reach. Let's delve into what makes our data stand out:

Unique Attributes: - From YouTube: Detailed video information including title, description, upload date, video ID, and channel URL. Video metrics such as views, likes, comments, and duration are also provided. - Creator Info: Access author details like name and channel URL. - Channel Information: Gain insights into channel title, description, location, join date, and visual branding elements like logo and banner URLs. - Channel Metrics: Understand a channel's performance with metrics like total views, subscribers, and video count. - Google Maps Integration: Explore business ratings from Google My Business and location data from Google Maps.

Data Sourcing: - Our data is meticulously sourced from publicly available information on YouTube and Google Maps, ensuring accuracy and reliability.

Primary Use-Cases: - Marketing: Analyze video performance metrics to optimize content strategies. - Research: Explore trends in creator behavior and audience engagement. - Location-Based Insights: Utilize Google Maps data for market research, competitor analysis, and location-based targeting.

Fit within Broader Offering: - This dataset complements our broader data offering by providing rich insights into online content consumption and geographical presence. It enhances decision-making processes across various industries, including marketing, advertising, research, and business intelligence.

Usage Examples: - Marketers can identify popular video topics and optimize advertising campaigns accordingly. - Researchers can analyze audience engagement patterns to understand viewer preferences. - Businesses can assess their Google My Business ratings and geographical distribution for strategic planning.

With scalable solutions and high-quality data, our dataset offers unparalleled depth for extracting actionable insights and driving informed decisions in the digital landscape.

YouTube Dislikes Dataset

This dataset contains information about trending YouTube videos from August 2020 to December 2021 for the USA, Canada, and Great Britain.

Context and Content

Youtube announced the decision to hide the number of dislikes from users around November 2021. However, the official YouTube Data API allowed you to get information about dislikes until December 13, 2021.

This dataset contains the latest possible information about dislikes, which was collected just before December 13. The information was collected by videos that had been trending in the USA, Canada, and Great Britain for a year prior.

The information is aimed at the English audience. In particular, all non-ASCII and non-Latin characters have been removed from the text fields.

The comments were received using the following query and combined into one string:

request = youtube.commentThreads().list(
        part="snippet",
        maxResults=20,
        order="relevance",
        textFormat="plainText",
        videoId=video_id)
response = request.execute()

order=relevance parameter is ignored when videoId is specified, so, basically, it's 20 random comments.

The code used to collect this dataset is available here.

To know more visit this GitLab repo.

Acknowledgements

This dataset was collected using the official YouTube Data API v3. Unique video IDs were extracted from YouTube Trending Video Dataset. Banner image - photo by Alexander Shatov on Unsplash.

Inspiration

Possible uses of this dataset may include a wide range of tasks: - Exploratory Data Analysis and Sentiment Analysis - Clustering YouTube videos - Training neural networks to analyze comments or video descriptions - and so on

This dataset and supplementary tables are released in conjunction with the TPDL 2025 paper titled “How Retracted Research Persists on YouTube: Retraction Severity, Visibility, and Disclosure.” They provide detailed information used in the analysis to promote transparency, ensure reproducibility, and facilitate future studies on scholarly communication and retractions.

The dataset contains the following files:

Filename	Data Format	Description
01_dataset_scholarly_references_on_YouTube.json.gz	JSON Lines	An integrated dataset of scholarly references in YouTube video descriptions, covering videos posted up to the end of December 2023. This dataset combines the Altmetric dataset and the YA Domain Dataset and is the basis for identifying references to retracted articles. This dataset contains 743,529 scholarly references (386,628 unique DOIs) found in 322,521 YouTube videos uploaded by 77,974 channels.
02_dataset_references_to_retracted_articles_on_YouTube.json.gz	JSON Lines	A dataset of retracted articles referenced in YouTube videos, used as the primary source for analysis in this paper. The dataset was created by cross-referencing the integrated reference dataset with the Retraction Watch database. It includes metadata such as DOI, article title, retraction reason, and severity classification (Severe, Moderate, or Minor) based on Woo and Walsh (2024), along with video- and channel-level statistics (e.g., view counts and subscriber counts) retrieved via the YouTube Data API v3 as of April 22, 2025. This dataset contains 1,002 retracted articles (360 unique DOIs) found in 956 YouTube videos uploaded by 714 channels.
03_full_list_table3_sorted_by_reference_count_retracted_articles_on_YouTube.json.gz	JSON Lines	Complete list corresponding to Table 3, "Top 7 retracted articles ranked by the number of YouTube videos in which they are referenced." in the paper.
04_full_list_table5_top10_most-viewed_video.json.gz	JSON Lines	Complete list corresponding to Table 5, "Top 10 most-viewed YouTube videos that reference retracted articles, sorted by video view count." in the paper.
05_detailed_manual_coding_40_sampled_retracted_articles.xlsx	XLSX	This file provides detailed annotations for a manually coded sample of 40 YouTube videos referencing retracted scholarly articles. The sample includes 10 randomly selected videos from each of the four analytical groups categorized by publication timing (before/after retraction) and retraction severity (Moderate/Severe). The file includes reference stance for each video, visual/verbal mention of the article, and relevant timestamps when applicable. This dataset supplements the manual analysis results presented in Tables 6 and 7 in paper.

Due to concerns over potential misuse (e.g., identification or harassment of individual content creators), this dataset is not made publicly available.
Researchers who wish to use this dataset for scholarly purposes may contact the authors to request access.

References

• Woo, S., Walsh, J.P.: On the shoulders of fallen giants: What do references to retracted research tell us about citation behaviors? Quantitative Science Studies 5(1), 1–30 (2024). https://doi.org/10.1162/qss_a_00303
• Kikkawa, J., Takaku, M.: How Retracted Article Persists on YouTube: Retraction Severity, Visibility, and Disclosure. Accepted for publication in the Proceedings of the 29th International Conference on Theory and Practice of Digital Libraries (TPDL 2025).
• Accepted Papers (TPDL2025) - https://tpdl2025.github.io/Program/accepted_papers.html

Fundings

JSPS KAKENHI Grant Numbers JP22K18147 and JP23K11761.

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.

1) Data Introduction • The YouTube Clickbait Classification dataset consists of video titles and statistics aimed at classifying videos as clickbait or not clickbait. This dataset includes attributes such as video ID, title, views, likes, dislikes, and favorites, providing a basis for binary classification tasks to identify misleading content.

2) Data Utilization (1) YouTube Clickbait data has characteristics that: • It includes detailed statistics for each video, such as views, likes, dislikes, and favorites, alongside the video titles. This information helps in understanding the engagement metrics and identifying patterns associated with clickbait content. (2) YouTube Clickbait data can be used to: • Content Analysis: Assists in developing models to classify videos as clickbait or not, helping in curating quality content and improving user experience on video platforms. • Marketing and SEO: Supports the development of strategies to enhance video reach and engagement while avoiding deceptive practices, aiding in ethical content marketing and search engine optimization.

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.

The YouTube channel of the Japanese entertainment company Avex Group Holdings Inc. counted approximately 7.8 billion total video views on its videos, making it the most popular domestic YouTube account in Japan as of August 2020. The YouTuber Hajime Syacho, who's videos were viewed approximately 6.8 billion times in total, had the second largest audience.

YouTube in Japan

The Japanese-language version of YouTube was released in 2007. The website has since become the most popular video-sharing platform in the country, reaching over 62 million monthly active users as of October 2018. Its competitors in Japan include domestic websites such as Nico Nico Douga, known for its bullet chatting feature, and AbemaTV, an online television network. While Japan has a relatively low social network penetration rate when compared to some of its neighboring countries, a survey conducted in 2021 found that YouTube had a penetration rate of over 97 percent among people in their teens and twenties.

“VTubers” as a recent YouTube trend

One recent trend in Japan are so-called “VTubers," which are virtual YouTubers created by motion capture technology. One of the most popular virtual YouTubers is Kizuna Ai, who debuted in 2016 and who is also credited with coining the term “VTuber." In 2018, she was appointed as an ambassador for the Japan National Tourism Organization (JNTO) campaign "Come to JAPAN" in order to increase American tourism to Japan. Other famous VTubers include Kaguya Luna and Shirakami Fubuki.

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.