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.

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!

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 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.

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total 232.5 million users (+24.91 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 1.2 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 150 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.

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.

Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

Youtube social network and ground-truth communities Dataset information Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

more info : https://snap.stanford.edu/data/com-Youtube.html

Cantonese Youtube Pseudo-Transcription Dataset

Contains approximately 10k hours of audio sourced from YouTube Videos are chosen at random, and scraped on a channel basis Includes news, vlogs, entertainment, stories, health

Columns transcript_whisper: Transcribed using Scrya/whisper-large-v2-cantonese with alvanlii/whisper-small-cantonese for speculative decoding transcript_sensevoice: Transcribed using FunAudioLLM/SenseVoiceSmall used OpenCC to convert to traditional chinese… See the full description on the dataset page: https://huggingface.co/datasets/alvanlii/cantonese-youtube.

During a March 2020 survey of social media users in the United States, 64 percent of respondents stated that if confined to their homes during the coronavirus, they would use YouTube more during that period. Many counties and cities in the United States have called upon residents to stay at home during the global coronavirus pandemic and subsequently, internet users turn to digital video and video-on-demand platforms to keep them entertained.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Introduction

YouTube Statistics: Ever since 2005, YouTube, starting as a small video-sharing website, has quickly grown into a global giant in entertainment, education, and marketing sectors. Now, as of 2024, the platform itself has expanded considerably in terms of its outreach and impact as it consolidates its dominance in the digital landscape.

This article takes into account the latest YouTube statistics and trends to show its triumphal growth and importance in several spheres.

markhneedham/youtube-comments dataset hosted on Hugging Face and contributed by the HF Datasets community

Raccoon Youtube

## Overview

Raccoon Youtube is a dataset for object detection tasks - it contains Raccoon annotations for 517 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Shresaw

Released under MIT

Contents

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.

deboleen6/youtube-dataset-multispeaker-11-may-24-new dataset hosted on Hugging Face and contributed by the HF Datasets community

ABSTRACT This research aims to analyze the use of Youtube as a useful platform for the activities of library and information science professionals in brazilian academic libraries. Related audiovisual practices of the university libraries to encourage activities and focus on the importance of the librarian as a content producer in the digital enviroment. The survey results serve as reference material for information scientists and managers of information units interested in sharing audiovisual information as a new way of relationship with their users. Finally, based on the results, it is recommended to plan the communication strategy on social media platforms as YouTube, and prepare relevant content to engage with their subscribers and users.

The chart number of videos removed by YouTube for the period October 2017-March 2022, by first source of detection (automated flagging or human detection). Flags from human detection can come from a user or a member of YouTube’s Trusted Flagger program,which include individuals, NGOs, and government agencies. The chart shows that the number of automated flagging is significantly higher compared to human detection. When it comes to human detection, the biggest number of removed videos were first noticed by users, followed by individual trusted flaggers, NGOs and government agencies.

Dataset

This dataset was created by Panda-monium

Released under MIT

Contents

We present an English YouTube dataset manually annotated for hate speech types and targets. The comments to be annotated were sampled from the English YouTube comments on videos about the Covid-19 pandemic in the period from January 2020 to May 2020. Two sets were annotated: a training set with 51,655 comments (IMSyPP_EN_YouTube_comments_train.csv) and two evaluation sets, one annotated in-context (IMSyPP_EN_YouTube_comments_evaluation_context.csv), another out-of-context (IMSyPP_EN_YouTube_comments_evaluation_no_context.csv), each based on the same 10,759 comments. The dataset was annotated by 10 annotators with most (99.9%) of the comments being annotated by two annotators. It was used to train a classification model for hate speech types detection that is publicly available at the following URL: https://huggingface.co/IMSyPP/hate_speech_en.

The dataset consists of the following fields: Video_ID - YouTube ID of the video under which the comment was posted Comment_ID - YouTube ID of the comment Text - text of the comment Type - type of hate speech Target - the target of hate speech Annotator - code of the human annotator