As of June 2022, more than 500 hours of video were uploaded to YouTube every minute. This equates to approximately 30,000 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 40 percent between 2014 and 2020.

YouTube global users

Online video is one of the most popular digital activities worldwide, with 27 percent of internet users worldwide watching more than 17 hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately 900 million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than 86 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 217.25 billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.

What is the most subscribed YouTube Channel?

Indian music network T-Series had the most YouTube subscribers in the world as of November 2022, with 229 million users following the channel. YouTube Movies ranked second with roughly 158 million subscribers.

How many hours of video are uploaded to YouTube every minute?

YouTube was launched in 2005 as a platform for sharing user-generated videos such as vlogs, tutorials, or original series. The site grew rapidly and reportedly had 100 million video views per day and more than 65 thousand daily uploads only a year later. As of February 2020, more than 500 hours of video were uploaded to YouTube every minute, up from a mere 24 hours of content uploads per minute in 2010.

Highest earning YouTubers

In November 2022, MrBeast surpassed long-standing most subscribed YouTuber PewDiePie, having reached approximately 112 million subscribers. Due to the high number of subscribers and even higher number of views, these out-of-the-box stars not only have millions of fans, but also considerable earnings from their YouTube activities. In 2021, MrBeast was estimated to have earned around 54 million U.S. dollars, topping the ranking of the highest-earning YouTube creators.

YouTube Partner Program

In the third quarter of 2022, YouTube’s ad revenue amounted to over seven billion U.S. dollars. Through its Partner Program, YouTube also rewards uploaders of popular videos with a share of the advertising revenues the content generates. This, paired with the fact that many users of the video sharing platform tend to have favorite channels that they revisit regularly, has given rise to another phenomenon: YouTube celebrities. Although some of these well-known figures were discovered on the website but then carved a successful career outside of YouTube, for many others the site is their primary platform for delivering content and staying in contact with fans, all while signing lucrative deals or promotional partnerships

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.

The YouTube-100M data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”). Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced.

Context

This dataset was extracted for one of the assignment during the Data Science course. This data is extracted from "https://www.youtube.com/c/ZeeshanUsmani78" . If someone interested in Python code that I have used to extract, you can view in my profile: "https://github.com/meayyaz/ParsingInPython/blob/main/ChannelData.py" This kind of data can help to Learn any Youtube channel statistics.

Content

Dataset : There are only 325 rows in this dataset and columns are "VideoId", "Title" (title of video), "PublishTime", "ViewCount", "LikeCount", "DislikeCount", "favoriteCount" , "commentCount"

Acknowledgements

I would like to Thanks Zeeshan-ul-hassan Usmani for allowing to upload this data and giving such a good live example.

Inspiration

I would like to learn Data Science and Machine Learning with my others fellows. Here I think we should get from this dataset: - Main target "After loading any new video, what will be the 'view-count', 'Like-count' in next 24 hours, after 7 days ... " - What kind of videos has more view? - Any relationship of Video publish timestamp?

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Trending video data from YouTube.

Video Object Properties

This dataset represents each trending video object with the following properties:

• title: The title of the video.
• description: The video's description.
• publishedDate: The date and time when the video was published (in a machine-readable format).
• publishedText: The date and time when the video was published (in a human-readable format).
• videoId: The unique identifier for the video.
• videoUrl: The URL of the video.
• channelName: The name of the channel that published the video.
• channelId: The unique identifier for the channel.
• channelUrl: The URL of the channel.
• thumbnails: URLs for the video's thumbnail images in different resolutions.
• views: The number of views the video has received.
• viewsText: The number of views in a human-readable format (e.g., "1.2M views").
• duration: The duration of the video (in a machine-readable format).
• durationText: The duration of the video in a human-readable format (e.g., "3:24").
• verified: A boolean indicating whether the channel is verified or not.
• creatorOnRise: A boolean indicating whether the channel is marked as a "Creator on the Rise" by YouTube.
• isShort: A boolean indicating whether the video is a YouTube Shorts video or not.

trending video data is collected from various categories on YouTube:

• Default: The main YouTube trending page, featuring videos across all categories.
• Music: The trending page specifically for music videos and music-related content.
• Gaming: The trending page for gaming videos, live streams, and gaming-related content.
• Movies: The trending page for movie trailers, clips, and other movie-related videos.

The collected data is saved daily in compressed CSV (Comma-Separated Values) files, with one file per category. The file naming convention follows the format: category_**timestamp**.csv.gz

Where:

• category is the name of the category (e.g., default, music, gaming, movies).
• timestamp is the current date and time in the format YYYYMMDD (e.g., 20230501 for May 1, 2023).

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.

Introduction

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The data id collected using YouTube Data Tools from BBC YouTube channel. It shows information about all videos from this channel, starting with 2007.

Data collection

Using YouTube Data Tools one can access the metadata for YouTube channels, videos, comments, upvotes.

References

• YouTube Data Tools, https://tools.digitalmethods.net/

Inspiration

Use this amazing dataset to analyze the impact of these videos, by looking to view, like, dislike, favorite, comments. Try to understand from description of the video if some subjects have larger impact. Factor-in the ”age” of each video, with this amazing dataset collecting video metadata starting from 2007.

YouTube keeps track of the most popular videos that are being seen on the site. Several months' worth of daily trending YouTube video statistics are included in this data set. Data for France and the USA are included. The videos on this list are those that users have liked and have received the most views, comments, and likes from other users. These videos are then displayed on the trending page. The greatest videos are shown at the top of the page by ranking these videos according to a ratio of views, likes, comments, and shares.

This dataset is a daily record of the top trending YouTube videos.

content: Data about daily trending YouTube videos for several months, and counting, is included in this dataset. Up to 200 trending videos are published each day, with data for the US and FR regions (the USA and France, respectively) included.

Extensive Creator Coverage: Our dataset includes a diverse range of YouTube content creators, spanning various genres, subscriber counts, and regions. Access information on creators from a wide spectrum of content categories.

Creator Profiles: Explore detailed creator profiles, including biographies, subscriber counts, video counts, and contact information.

Customizable Data Delivery: The dataset is available in flexible formats, such as CSV, JSON, or API integration, allowing seamless integration with your existing data infrastructure. Customize the data to meet your specific research and analysis needs.

📺 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.

Over the past few years YouTube has became a popular site for video broadcasting and earning money by publishing various different skills in the form of videos. For some people it has become a main source to earn money. Getting the videos trending among the viewers is one of the major tasks which each and every content creator wants. Popularity of any video and its reach to the audience is completely based on YouTube's Recommendation algorithm. This document is a dataset descriptor for the dataset collected over the time span of about 45 days during the Israel-Hamas War

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!

Sphere360

Sphere360 is a comprehensive dataset of paired 360-degree videos and spatial audio content sourced from YouTube. The collection contains over 103,000 matched 360-degree video and audio clips, representing a total of 288 hours of immersive content. This repository includes both the curated dataset and the essential web crawling and data processing tools used for its compilation.

Sphere360 Copyright Dataset Split Toolset Environment Python Environment YouTube API FFmpeg… See the full description on the dataset page: https://huggingface.co/datasets/omniaudio/Sphere360.

ITTV is a publicly available dataset of Italian TV programs introduced in

Alessandro Ilic Mezza, Paolo Sani, and Augusto Sarti, "Automatic TV Genre Classification Based on Visually-Conditioned Deep Audio Features," in 2023 31st European Signal Processing Conference (EUSIPCO), 2023.

ITTV consists of 2625 manually annotated YouTube videos, totaling over 670 hours. Each clip is assigned one of seven classes:

Cartoons

Commercials

Football

Music

News

Talk Shows

Weather Forecast

ITTV genre taxonomy is similar to that of the well-known RAI dataset described in

Maurizio Montagnuolo and Alberto Messina, "Parallel neural networks for multimodal video genre classification,” Multimedia Tools and Applications, vol. 41, no. 1, pp. 125–159, 2009.

The dataset contains genre annotations and metadata in CSV format. Please note that audio data is not provided.

We provide the annotations for a balanced training (1575 clips) and validation (525 clips) split, as well as for a disjoint test set containing 525 installments from TV programs not included in the development set.

As YouTube continuously updates, some videos may not be available in the future. Although we intend to keep ITTV updated as best as possible, please note that some content may not be available at any given time.

Some YouTube videos (especially from the Football class and, to a lesser extent, the Cartoons class) may only be available in some countries due to regional restrictions imposed by the content creator. All videos are known to be accessible from Italy (last accessed on Nov. 25th, 2022.)

Please contact Alessandro Ilic Mezza for further questions (e-mail: alessandroilic.mezza@polimi.it).

Context

As YouTube is now one of the biggest online earning platform for content creators, lots of new content creators join everyday and upload almost thousands of video daily, which creates enormous amount of data everyday, from which we can do lots of things. Here I have taken data of T-Series, one of the most subscribed channel on YouTube, it's views and ratings of its past video and estimate its revenue for each video.

There's a story behind every dataset and here's your opportunity to share yours.

Content

There are very less features in this dataset, namely: Date: The date when the particular video was released Name: Name of the video on YouTube Views: The views on YouTube as per December 2020 Ratings: The ratings of the video Comments: Number of comments on the video Estimated Revenue: The revenue generated by the video on YouTube What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

This data search wouldn't be possible without my sister as she was constantly watching videos on YouTube which lead me to this idea and then started working on this dataset.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

In recent years, video has become one of the most popular online formats, spanning from educational content to product reviews. During the third quarter of 2024, music videos recorded the highest category reach, with almost half of internet users worldwide reporting to watch music videos online each week. Social video engagement In recent years, YouTube and TikTok have become two of the most important social media platforms for global users, as video content commands high levels of engagement. In 2024, users worldwide spent approximately 28.4 hours using the YouTube mobile app per month. Additionally, the leading hashtags used by content creators on TikTok have amassed billions of views: as of January 2024, the TikTok hashtag “fyp” or “for you page” had reached 55 and 35 billion post views, respectively. Watching content: what device do users prefer? In 2023, televisions were the most used devices for global viewers to watch video-on-demand (VOD), with 55 percent of respondents reporting using these devices. In comparison, 13 percent of respondents reported using smartphones. Age group and generation are factors impacting viewership habits and device preferences, as younger users appear to prefer using their smartphones to consume content. According to a March 2024 survey, U.S. users aged 18-34 years were more likely to watch video content on smartphones than any other devices. By comparison, connected TVs were particularly popular for the online video audience aged 35 and older.

VideoGameBunny Instruction Following Dataset

Paper - Website

  Overview

We present a comprehensive dataset of 185,259 high-resolution images from 413 video games, sourced from YouTube videos. This dataset addresses the lack of game-specific instruction-following data and aims to improve the ability of open-source models to understand and respond to video game content.

  Dataset Composition

Our dataset includes various types of instructions generated for these… See the full description on the dataset page: https://huggingface.co/datasets/asgaardlab/VideoGameBunny-Dataset.

Context

This dataset is a daily record of the top trending YouTube videos, The data was collected for several countries : Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates.

Data was collected during 12th of November 2019 & 21th of November 2019

Content

This dataset includes data of daily trending YouTube videos. Data is included for Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates. Each region’s data is in a separate file.

Data includes the video title, video id , channel title, tags, views, and video length in minutes.

Notes

Since the duration column is measure of video length in minutes, any video with length less than 1 minute its length attribute was stored as 0 .