This dataset contains information about trending YouTube videos, including details about the videos and their respective channels. The data is collected daily and provides insights into video performance, audience engagement, and channel characteristics across different countries. Below is a detailed description of each column:

Video Information

1. video_id: Unique identifier for the video on YouTube.
2. video_published_at: The date and time when the video was published.
3. video_trending_date: The date when the video was identified as trending.
4. video_trending_country: The country where the video is trending (ISO 3166-1 alpha-2 country code, e.g., "US" for the United States).
5. video_title: The title of the video as displayed on YouTube.
6. video_description: The description provided by the video creator.
7. video_default_thumbnail: URL of the default thumbnail for the video.
8. video_category_id: Numeric ID representing the category of the video (e.g., Music, Gaming, etc.).
9. video_tags: List of tags associated with the video for categorization and discoverability.
10. video_duration: Duration of the video in ISO 8601 format (e.g., "PT10M15S" for 10 minutes and 15 seconds).
11. video_dimension: Dimension of the video (e.g., "2d", "3d").
12. video_definition: Video resolution quality (e.g., "hd" for high definition, "sd" for standard definition).
13. video_licensed_content: Boolean indicating if the video contains licensed content.
14. video_view_count: Total number of views for the video.
15. video_like_count: Total number of likes for the video.
16. video_comment_count: Total number of comments on the video.

Channel Information

1. channel_id: Unique identifier for the YouTube channel.
2. channel_title: The name/title of the channel.
3. channel_description: Description provided by the channel owner.
4. channel_custom_url: Custom URL for the channel (if available).
5. channel_published_at: The date and time when the channel was created.
6. channel_country: The country associated with the channel (if specified by the creator).
7. channel_view_count: Total number of views across all videos on the channel.
8. channel_subscriber_count: Total number of subscribers to the channel.
9. channel_have_hidden_subscribers: Boolean indicating if the channel has hidden its subscriber count.
10. channel_video_count: Total number of videos uploaded by the channel.
11. channel_localized_title: The localized title of the channel (if available in a different language).
12. channel_localized_description: The localized description of the channel (if available in a different language).

Potential Applications

This dataset is a rich resource for analyzing YouTube video and channel trends. Here are some potential use cases:

1. Trend Analysis:

   • Identify trending content in specific countries or globally.
   • Explore the relationship between publishing time and trending status.

2. Audience Engagement Insights:

   • Analyze the correlation between video views, likes, and comments.
   • Understand the impact of video tags, duration, or definition on engagement.

3. Content Category Insights:

   • Explore how different video categories perform over time.
   • Analyze the popularity of certain categories in specific regions.

4. Channel Growth Analysis:

   • Study how trending videos impact channel subscriber growth.
   • Analyze the characteristics of successful channels (e.g., average video duration, view count, etc.).

5. Machine Learning Projects:

   • Predict the likelihood of a video trending based on its metadata.
   • Cluster videos or channels based on similarities in tags, categories, and engagement metrics.

6. Business Applications:

   • Help content creators and marketers understand audience preferences.
   • Use data to guide video production strategies for better engagement.

Additional Notes

This dataset can be combined with other external datasets, such as demographic or social media engagement data, for broader analyses. It is particularly suitable for projects related to content strategy, audience analysis, or even recommendation system development for platforms similar to YouTube.

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

Description

Welcome to the captivating realm of YouTube stardom, where this meticulously curated dataset unveils the statistics of the most subscribed YouTube channels. A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.

For more related datasets:
Global Missing Migrants Dataset( New ) 🛑
"https://www.kaggle.com/datasets/nelgiriyewithana/global-weather-repository">Global Weather Repository ( Daily snapshot ) ( New )🛑
"https://www.kaggle.com/datasets/nelgiriyewithana/indian-weather-repository-daily-snapshot">Indian Weather Repository ( Daily snapshot ( New ) ) 🛑

Key Features

• rank: Position of the YouTube channel based on the number of subscribers
• Youtuber: Name of the YouTube channel
• subscribers: Number of subscribers to the channel
• video views: Total views across all videos on the channel
• category: Category or niche of the channel
• Title: Title of the YouTube channel
• uploads: Total number of videos uploaded on the channel
• Country: Country where the YouTube channel originates
• Abbreviation: Abbreviation of the country
• channel_type: Type of the YouTube channel (e.g., individual, brand)
• video_views_rank: Ranking of the channel based on total video views
• country_rank: Ranking of the channel based on the number of subscribers within its country
• channel_type_rank: Ranking of the channel based on its type (individual or brand)
• video_views_for_the_last_30_days: Total video views in the last 30 days
• lowest_monthly_earnings: Lowest estimated monthly earnings from the channel
• highest_monthly_earnings: Highest estimated monthly earnings from the channel
• lowest_yearly_earnings: Lowest estimated yearly earnings from the channel
• highest_yearly_earnings: Highest estimated yearly earnings from the channel
• subscribers_for_last_30_days: Number of new subscribers gained in the last 30 days
• created_year: Year when the YouTube channel was created
• created_month: Month when the YouTube channel was created
• created_date: Exact date of the YouTube channel's creation
• Gross tertiary education enrollment (%): Percentage of the population enrolled in tertiary education in the country
• Population: Total population of the country
• Unemployment rate: Unemployment rate in the country
• Urban_population: Percentage of the population living in urban areas
• Latitude: Latitude coordinate of the country's location
• Longitude: Longitude coordinate of the country's location

Potential Use Cases

• YouTube Analytics: Gain valuable insights into the success factors of top YouTube channels and understand what sets them apart from the rest.
• Content Strategy: Discover the most popular categories and upload frequencies that resonate with audiences.
• Regional Influencers: Identify influential YouTube creators from different countries and analyze their impact on a global scale.
• Earnings Analysis: Explore the correlation between channel performance and estimated earnings.
• Geospatial Visualization: Visualize the distribution of successful YouTube channels on a world map and uncover geographical trends.
• Trending Topics: Investigate how certain categories gain popularity over time and correlate with world events.

Data Source: The dataset was meticulously compiled from various reputable sources, ensuring accuracy and reliability of the information presented.

If you find this dataset helpful, your support through an upvote would be deeply appreciated ❤️

This dataset provides an in-depth look at YouTube video analytics, capturing key metrics related to video performance, audience engagement, revenue generation, and viewer behavior. Sourced from real video data, it highlights how variables like video duration, upload time, and ad impressions contribute to monetization and audience retention. This dataset is ideal for data analysts, content creators, and marketers aiming to uncover trends in viewer engagement, optimize content strategies, and maximize ad revenue. Inspired by the evolving landscape of digital content, it serves as a resource for understanding the impact of YouTube metrics on channel growth and content reach.

Video Details: Columns like Video Duration, Video Publish Time, Days Since Publish, Day of Week.

Revenue Metrics: Includes Revenue per 1000 Views (USD), Estimated Revenue (USD), Ad Impressions, and various ad revenue sources (e.g., AdSense, DoubleClick).

Engagement Metrics: Metrics such as Views, Likes, Dislikes, Shares, Comments, Average View Duration, Average View Percentage (%), and Video Thumbnail CTR (%).

Audience Data: Data on New Subscribers, Unsubscribes, Unique Viewers, Returning Viewers, and New Viewers.

Monetization & Transaction Metrics: Details on Monetized Playbacks, Playback-Based CPM, YouTube Premium Revenue, and transactions like Orders and Total Sales Volume (USD).

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

In October 2006, 18 months after posting its first video and 10 months after its official launch, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone, to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube and approved creators participate in Google's AdSense program, which seeks to generate more revenue for both parties. YouTube reported revenue of $19.8 billion in 2020. In 2021, YouTube's annual advertising revenue increased to $28.8 billion.

This dataset consists details on top 1000 influencers all over the world.

Comprehensive YouTube channel statistics for RJ UPLOADED, featuring 200,000 subscribers and 33,949,706 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in IN. Track 484 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Context

I love teaching Data Science and love doing through my channel. I have learned a lot from Youtube, and now I thought I could put some of my data on Kaggle to analysed. It would be fun to see perspectives others able to generate. ✋

Content

I have uploaded 13 tables which are directly downloaded from my YouTube Channel. The datasets have daily data since the start of my channel: views, hours of views, revenue, demographics and more.

Inspiration

I have spent a lot of time on this data and would love our support on how best I could improve hours of viewing.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

Youtube is very much used to influence, educate, free university (for me also) people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is currently owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

Youtube is very much used to influence people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

Data Dictionary

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?

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.

context:

YouTube has become the largest stage for music in the world, with millions of artists, songs, and performances uploaded and streamed every day. Music videos are among the platform’s most popular content, often reaching billions of views and shaping global pop culture. This dataset was created to capture a snapshot of that ecosystem by collecting details about 100 popular music videos and the channels that host them.

The goal is to provide a resource for exploring how music spreads on YouTube — whether that’s through analyzing engagement, comparing artists, or uncovering trends in the way music content is presented.

content:

Each row in the dataset represents a music video, enriched with both video-level information and channel-level details:

title, fulltitle → Video titles

description → Official video description text

view_count → Total number of views

categories → YouTube category (e.g., Music)

tags → Keywords or labels assigned to the video

duration, duration_string → Video duration in seconds and formatted string

live_status → Indicates whether the video was streamed live

thumbnail → Thumbnail image URL

channel, channel_url → Channel name and direct link

channel_follower_count → Subscriber count of the channel

This dataset can be used for a variety of projects, such as:

• Analyzing video popularity and audience engagement

• Comparing artists and channels across different metrics

• Text mining and NLP on titles, tags, and descriptions

• Predictive modeling of video views and trends

Acknowledgement:

The data was collected from publicly available information on YouTube and is provided for educational and research purposes only. All rights to the original videos and content belong to their respective creators.

Comprehensive YouTube channel statistics for MrBeast, featuring 452,000,000 subscribers and 101,702,564,900 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in US. Track 923 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

💜 Context

This dataset consists of YouTube video and comment metadata for 💜 BTS 💜 related videos in January 2021

BTS has become a global phenomenon as their music, dances, and spirit attracts worldwide fans. YouTube has become one of KPOP's main social media platform for both artists and fans to share their work and enthusiasm. From dance covers to reaction videos, this dataset collects metadata from BTS related videos that were uploaded in January, 2021.

📈 Dataset Description

The videos in this dataset are between January 1st, 2021 and January 30th, 2021. The dataset contains the most-viewed videos uploaded each day that contained the keyword BTS.

🔧 Collection Method

For this project, I developed youcos, a simple Python package for collecting and saving YouTube video and comment data through the YouTube v3 API. Feel free to contribute to the project or use the package to collect your own data!

👍 Acknowledgements

The data was primarily acquired through the YouTube v3 API. You can read their Terms of Service here The BTS Banner was sourced from here

🌱 Inspiration

Here are some possible next steps for this data: - Perform sentiment analysis for the videos and comments, - Compare sentiments for COVID related posts with Twitter, Reddit, and other social media platforms - Predict the number of comments, views, and likes/dislikes a video will have based on its Title - Predict the number of likes a comment will receive based on its text and sentiments

📔 Note

future datasets / additions will include data collected based on a specific time frame, location, and view counts I also have a similar YouTube Comments Dataset for COVID-19

If you find this dataset useful, please UPVOTE! It motivates me to create more quality content. Thank you!

Dataset Description:

Column1: Video id of 11 characters. Column2: uploader of the video of string data type. Column3: Interval between day of establishment of Youtube and the date of uploading of the video of integer data type. Column4: Category of the video of String data type. Column5: Length of the video of integer data type. Column6: Number of views for the video of integer data type. Column7: Rating on the video of float data type. Column8: Number of ratings given on the video. Column9: Number of comments on the videos in integer data type. Column10: Related video ids with the uploaded video.

Context

Hello User, welcome to deepfake dataset. I started exploring about deepfakes somewhere in January 2019. Deepfakes developed using Deep Learning were relatively new back then. Gradually, I started learning about GAN's, auto-encoders, adversarial auto-encoders to know more about deepfakes and their detection. Eventually, I ended up creating a deepfake to use it as a dataset for detection purpose.

Content

The dataset is really simple and contains 4 videos (MP4 format): 1. Real Video The realvideo.mp4 is a clip from The Daily Show with Trevor Noah. 2. Fake Video The fakevideo.mp4 is the actual deepfake generated by swapping Trevor Noah's face with President Barack Obama's face. 3. Horizontal comparison video The df_horizontal.mp4 has real video (left side) and fake video (right side) together aligned side by side for frame by frame comparison. 4. Vertical comparison video
The df_vertical.mp4 has real video (top) and fake video (bottom) together for frame by frame comparison.

Acknowledgements

My friends played a big role in helping me with Python scripts to generate the deepfake. It was a combined team efforts of the entire team. I thank my team. Also, I thank the The Daily Show with Trevor Noah's Youtube Channel for uploading the video.

Inspiration

This dataset was created with a motive to generate a technique for deepfakes detection. I'll be really happy to see a technique which detects suspected deepfakes. I envision/endeavour to build a software which calculates the percentage (0-100%) of suspected fakeness of a video being input. Please do NOT spread deepfakes and I'm glad to share my dataset with the Kaggle community. 😄 The question I would like to ask is: (The question is with an assumption that fake and real videos are available to us) 1. Can an unsupervised DL model be trained with victim's facial features, and later fed with the frames from a fake video which will show the altered parts of victim's face and calculate the percentage of fakeness from that frame?

Comprehensive YouTube channel statistics for Safa Islamic, featuring 8,630,000 subscribers and 2,249,927,536 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Religion category and is based in AE. Track 1,036 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.