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

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 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.

In 2021, YouTube's user base in the United States amounts to approximately 203.80 million users. The number of YouTube users in the United States is projected to reach 219.28 million users by 2025. User figures 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).

The number of Youtube users in India was forecast to continuously increase between 2024 and 2029 by in total 222.2 million users (+34.88 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 859.26 million 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 Sri Lanka and Nepal.

As of February 2025, ** percent of the YouTube global audience was composed of male users aged between 25 and 34 years, as well as around *** percent of female users of the same age. Male users aged between 35 and 44 years on the platform accounted for **** percent of the total, while women of the same age using YouTube had an audience share of *** percent in the examined period. YouTube’s global popularity The number of monthly active users on YouTube reached almost *** billion in April 2024, making it the second most popular social network on the internet. The platform's popularity spans all over the world, with India and the United States having the largest YouTube audiences. As of April 2024, the audience of YouTube in India was around *** million, while the United States recorded a YouTube audience of around *** million users.

YouTube’s digital revenues One of YouTube's leading monetization methods include advertising, with the company generating around **** billion U.S. dollars in the first quarter of 2024. Additionally, the platform generated over ** million dollars in the United States through in-app purchases, as well as over **** million U.S. dollars in revenues from mobile app users in Japan.

This YouTube dataset is a sampling from thousands of User Generated Content (UGC) as uploaded to YouTube distributed under the Creative Commons license. This dataset was created in order to assist in the advancement of video compression and quality assessment research of UGC videos.

Regarding the issue of increasing medical schools in Korea, YouTube video data was collected to track who is spreading news and to measure user responses as events unfold.

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

In 2021, YouTube's user base in the United Kingdom amounts to approximately ***** million users. The number of YouTube users in the United Kingdom is projected to reach ***** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

A comprehensive dataset of over 157 up-to-date YouTube statistics for 2025, covering user behavior, demographics, creator earnings, ad performance, Shorts, and AI content trends. Compiled from credible sources such as Pew Research, Statista, eMarketer, Google, and YouTube.

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

In 2021, YouTube's user base in Vietnam amounts to approximately ***** million users. The number of YouTube users in Vietnam is projected to reach ***** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Introduction

We introduce YTCommentVerse, a large-scale multilingual and multi-category dataset of YouTube comments. It contains over 32 million comments from 178,000 videos contributed by more than 20 million unique users spanning 15 distinct YouTube content categories such as Music, News, Education and Entertainment. Each comment in the dataset includes video and comment IDs, user channel details, upvotes and category labels. With comments in over 50 languages,
YTCommentVerse provides a rich resource for exploring sentiment, toxicity and engagement patterns across diverse cultural and topical contexts. This dataset helps fill a major gap in publicly available social media datasets particularly for analyzing video sharing platforms by combining multiple languages, detailed categories and other metadata.

Data Description

Each entry in the dataset is related to one comment for a specific YouTube video in the related category with the following columns: videoID, commentID, commenterName, commenterChannelID, comment, votes, originalChannelID, category. Each field is explained below:

videoID: represents the video ID in YouTube.
commentID: represents the comment ID.
commenterName: represents the name of the commenter.
commenterChannelID: represents the ID of the commenter.
comment: represents the comment text.
votes: represents the upvotes received by that comment.
originalChannelID: represents the original channel ID who posted the video.
category: represents the category of the YouTube video.

Data Anonymization

The data is anonymized by removing all Personally Identifiable Information (PII).

Data sample

{
"videoID": "ab9fe84e2b2406efba4c23385ef9312a",
"commentID": "488b24557cf81ed56e75bab6cbf76fa9",
"commenterName": "b654822a96eae771cbac945e49e43cbd",
"commenterChannelID": "2f1364f249626b3ca514966e3ef3aead",
"comment": "ich fand den Handelwecker am besten",
"votes": 2,
"originalChannelID": "oc_2f1364f249626b3ca514966e3ef3aead",
"category": "entertainment"
}

Multilingual data

| Language | Text |

|--------------|---------------------------------------------------|

| English | You girls are so awesome!! |

| Russian | Точно так же Я стрелец |

| Hindi | आज भी भाई कʏ आवाज में वही पुरानी बात है.... |

| Chinese | 無論如何,你已經是台灣YT訂閱數之首 |

| Bengali | খুিন হািসনােক ভারেতর àধানমন্... |

| Spanish | jajajaj esto tiene que ser una brom |

| Portuguese | nossa senhora!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!... |

| Malayalam | നമസ്കാരം |

| Telegu | నమసాక్రం |

| Japanese | こんにちは |

Facebook is fast approaching 3 billion monthly active users. That’s about 36% of the world’s entire population that log in and use Facebook at least once a month.

Youtube online social network - Youtube is a video-sharing web site that includes a social network. The dataset contains a list of all of the user-to-user links.

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 data set was prepared from 88 open-source YouTube cooking videos. The YouCook dataset contains videos of people cooking various recipes. The videos were downloaded from YouTube and are all in the third-person viewpoint; they represent a significantly more challenging visual problem than existing cooking and kitchen datasets (the background kitchen/scene is different for many and most videos have dynamic camera changes). In addition, frame-by-frame object and action annotations are provided for training data (as well as a number of precomputed low-level features). Finally, each video has a number of human provided natural language descriptions (on average, there are eight different descriptions per video). This dataset has been created to serve as a benchmark in describing complex real-world videos with natural language descriptions.

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