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 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.

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.

YouTube is the world's largest video-sharing platform, launched in 2005. It allows users to upload, view, and share videos, and has grown to be a central hub for content creators across various fields, including entertainment, education, music, and more. With over 2 billion logged-in users monthly, YouTube has become an essential platform for digital content and marketing.

The Top 1000 YouTube Channels Dataset captures detailed information about the top-performing YouTube channels globally. This dataset includes the following columns:

• Rank : The ranking of the YouTube channel based on its overall popularity and performance.
• Youtuber : The name of the YouTuber or the title of the YouTube channel.
• Subscribers : The total number of subscribers to the channel, indicating its reach and popularity.
• Video Views : The total number of video views the channel has accumulated, reflecting its engagement and audience interaction.
• Video Count : The total number of videos uploaded by the channel, showing the content volume produced.
• Category : The genre or category the channel belongs to, such as music, education, entertainment, etc.
• Started : The year the channel was created, providing insight into its longevity and growth over time.

This dataset is invaluable for analyzing trends, understanding content strategies, and benchmarking channel performances within the YouTube ecosystem.

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

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.

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?

id, title and youtube segmentation of videos from the official youtube RAI channel (https://www.youtube.com/@rai) longer than 5 minutes. For each video the segmentation is a list composed by the start time (in milliseconds) and the title of each chapter. The dataset is already divided in two non-overlapping sets: 614 in "test_yt_over5min.json" and 2460 in "train_yt_over5min.json".

MeLa BitChute is a near-complete dataset of over 3M videos from 61K channels over 2.5 years (June 2019 to December 2021) from the social video hosting platform BitChute, a commonly used alternative to YouTube. Additionally, the dataset includes a variety of video-level metadata, including comments, channel descriptions, and views for each video.

The dataset contains data from 3,036,190 videos, 61,229 channels, and 11,434,571 comments between June 28th, 2019 and December 31st, 2021. This dataset provides timestamped activities and estimates on views for the majority of channels and videos on the platform, allowing researchers to align BitChute videos with behavior on other platforms. Therefore, this dataset can facilitate both studies of BitChute in isolation and studies of BitChute’s role in the larger ecosystem.

The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel.

Both the datasets comments are divided into seven categories:-

Label 1- Gratitude

Label 2- About the recipe

Label 3- About the video

Label 4- Praising

Label 5- Hybrid

Label 6- Undefined

Label 7- Suggestions and queries

All the labelling has been done manually.

Nisha Madhulika dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

Kabita Kitchen dataset:

Dataset characteristics: Multivariate

Number of instances: 4900

Area: Cooking

Attribute characteristics: Real

Number of attributes: 3

Date donated: March, 2019

Associate tasks: Classification

Missing values: Null

There are two separate datasets file of each channel named as preprocessing and main file .

The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:

• Id
• Comment text
• Labels

• Count of stop-words
• Uppercase words
• Hashtags
• Word count
• Char count
• Average words
• Numeric

The main file includes:

• Id
• comment text
• Labels

Please cite the paper

https://www.mdpi.com/2504-2289/3/3/37

MDPI and ACS Style

Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. Big Data Cogn. Comput. 2019, 3, 37.

Context

5-Minute Crafts is the Top 10 Most Viewed and Subscribed channel and this is what amazed me. I want to find the insights which lead the success of the channel.

Content

The data represents the Video Meta data, description, tags and most important statistics of the video.

Acknowledgements

Youtube and 5-Minute Crafts Channel

Inspiration

Most liked topic in the channel. View, Like and Comment count based on the video tags? What does the description say about the video? What are the most used tags?

The dataset contains the following files:

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.

Dataset Card for Platforma Video Dataset

  Dataset Summary

This dataset was scraped from video pages on the Russian video-sharing platform Platforma, a Russian YouTube alternative. It includes information about 181,876 videos across 12,341 channels. The dataset contains detailed information about each video and its associated channel, providing a comprehensive view of the content available on the platform.

  Languages

The dataset is primarily in Russian, but there… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/plvideo.

Context

YouTube is an American online video-sharing platform headquartered in San Bruno, California. The service, created in February 2005 by three former PayPal employees—Chad Hurley, Steve Chen, and Jawed Karim—was bought by Google in November 2006 for US$1.65 billion and now operates as one of the company's subsidiaries. YouTube is the second most-visited website after Google Search, according to Alexa Internet rankings.

YouTube allows users to upload, view, rate, share, add to playlists, report, comment on videos, and subscribe to other users. Available content includes video clips, TV show clips, music videos, short and documentary films, audio recordings, movie trailers, live streams, video blogging, short original videos, and educational videos.

YouTube (the world-famous video sharing website) 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”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for.

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

Note that this dataset is a structurally improved version of this dataset.

Acknowledgements

This dataset was collected using the YouTube API. This Description is cited in Wikipedia.

This dataset provides a detailed collection of video titles from the popular YouTube channel, 5-Minute Crafts, which is owned by TheSoul Publishing. As of October 2021, the channel was notably the 9th most-subscribed and one of the most-viewed channels on the platform [1]. While known for its DIY-style content, 5-Minute Crafts has faced criticism for unusual or potentially risky 'life hacks' and its heavy use of clickbait [1]. Despite this, the videos consistently achieve a high volume of views [1]. The dataset includes each video's title alongside various meta-features, such as total views, video duration, and the sentiment associated with the title [1]. It is designed for analysis to explore the relationship between words used in titles and views garnered, identify key title features that impact viewership, and examine correlations between title meta-features, total views, duration, and sentiment [1].

Columns

• video_id: A unique identifier for each video [2].
• title: The textual title of the video [2].
• active_since_days: The number of days the video has been active [2].
• duration_seconds: The length of the video in seconds [2].
• total_views: The overall count of views for the video [2].
• num_chars: The total number of characters present in the video title [2].
• num_words: The total count of words within the video title [2].
• num_punctuation: The number of punctuation marks in the title [2].
• num_words_uppercase: The count of words written entirely in uppercase within the title [2].
• num_words_lowercase: The count of words written entirely in lowercase within the title [2].

Distribution

The dataset comprises 4,978 unique video records from the 5-Minute Crafts YouTube channel, with 4,965 unique video titles [2]. * Video Duration: The duration of videos ranges from approximately 1 second to 1,460 seconds (about 24 minutes), with the majority falling between 1022.30 and 1168.20 seconds [3]. * Total Views: View counts range from 4,034 up to 283 million views, with most videos having between 4,034 and 28,306,741.50 views [4, 5]. * Title Characters: Video titles typically contain between 11 and 100 characters, with the most common length being 37.70 to 46.60 characters [5, 6]. * Title Words: Titles usually have between 3 and 20 words, with a peak concentration between 6.40 and 8.10 words [6, 7]. * Punctuation: The number of punctuation marks in titles ranges from 0 to 6, with most titles having very few, specifically between 0 and 0.60 punctuation marks [7]. * Uppercase Words: Titles contain between 0 and 18 uppercase words, with a notable concentration between 5.40 and 7.20 uppercase words [7, 8]. * Lowercase Words: The number of lowercase words in titles ranges from 0 to 12, with the majority of titles having between 0 and 1.20 lowercase words [8].

Usage

This dataset is well-suited for various analytical and modelling tasks, including: * Investigating the correlation between specific words used in titles and the total views generated [1]. * Identifying which features of a video title are most impactful in driving views [1]. * Exploring the relationships between title meta-features (like character or word count), total views, video duration, and sentiment [1]. * Developing predictive models for video performance based on title characteristics. * Performing natural language processing (NLP) tasks on video titles [1].

Coverage

The dataset focuses on videos from the 5-Minute Crafts YouTube channel [2]. * Geographic Scope: The data is globally relevant, reflecting the channel's international reach [9]. * Time Range: The dataset includes an 'active since days' column for each video, indicating its age, though specific calendar dates for data collection are not provided [1, 2].

License

CCO

Who Can Use It

This dataset is ideal for: * Data Scientists and Analysts: For developing and testing models related to content engagement and virality. * Content Creators and Marketers: To gain insights into effective title strategies and audience engagement on YouTube. * Researchers: Studying online media trends, clickbait phenomena, and the dynamics of popular DIY content. * AI/ML Developers: For training and validating NLP models on large-scale text data related to video titles [1].

Dataset Name Suggestions

• 5-Minute Crafts YouTube Performance Data
• YouTube Video Title Analysis Dataset
• Clickbait and Views Dataset
• DIY Content Engagement Metrics
• Online Video Analytics Data

Attributes

Original Data Source: 5-Minute Crafts: Video Clickbait Titles?

Analysis of ‘Shogi Channel's Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/satoshiss/shogi-channels-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This data came from a popular Youtube channel by a professional shogi player, called Shogi Hourouki. https://www.youtube.com/channel/UC9Ije5dQVFx9uTGddG_U5XA

If you are interested in Shogi(Japanese Chess), please check this channel.

Content

The dataset includes channel name, video titles, views, time, and url. I used Selenium to extract data. I made a notebook about the process. https://www.kaggle.com/satoshiss/web-scraping-on-a-youtube-channel-with-selenium

Inspiration

This is my very first dataset. It might be good for ExpIanatory Data analysis. I will add some more features(tags, count of like and dislike) later.

--- Original source retains full ownership of the source dataset ---

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).

This dataset contains anonymized logs of user-level YouTube viewing activity, collected via Amazon Mechanical Turk. Each user in the dataset provided at least six months of their YouTube watch history, enabling longitudinal analysis of personal viewing patterns.

Each row in the dataset represents a single watch event and includes metadata such as: - the video ID - watch timestamp - whether the user was subscribed to the channel at the time - and whether the video was part of a playlist

This dataset is intended to support research in user behavior modeling, content recommendation systems, temporal video engagement, and personalized analytics.

The dataset accompanies the paper:

"A YouTube dataset with user-level usage data: Baseline characteristics and key insights"
Authors: Shruti Lall, Mohit Agarwal, Raghupathy Sivakumar
Conference: IEEE ICC 2020 – International Conference on Communications

If you use this dataset in your research, please cite the paper above.

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 |

TV5 (also known as 5 and formerly known as ABC) is a Philippine free-to-air television and radio network. It is headquartered in Mandaluyong, Philippines, with alternate studios located in Novaliches, Quezon City, Philippines. TV5 serves as the flagship property of TV5 Network, Inc., which is owned by MediaQuest Holdings, the multimedia arm of PLDT, a telecommunications company. The network is commonly referred to as "The Kapatid Network", using the Filipino term for "sibling", a branding introduced in 2010. Sample Video

Official YouTube Channel https://www.youtube.com/@TV5Philippines

Important Note As you may have noticed, the channel has 11K videos but we only have 560+ in this dataset. This is because the API itself doesn't return all the videos as explained in this Stackoverlow post.

Image Generated with Bing Image Generator

License

CC0

Original Data Source: TV5 Philippines Youtube Channel Comments