YouTube Trending Videos Dataset

Exploring YouTube Trending Videos

By dskl [source]

About this dataset

Moreover it also reveals various engagement metrics such as the number of views the video has received, likes and dislikes it has garnered from viewership. Additionally information related to comment count on particular videos enables analysis regarding viewer interaction and response. Furthermore this dataset describes whether comments or ratings are disabled for a particular video allowing examination into how these factors impact engagement.

By exploring this dataset in-depth marketers can gain valuable insights into identifying trends in content popularity across different countries while taking into account timing considerations based on published day of week. It also opens up avenues for analyzing public sentiment towards specific videos based on likes vs dislikes ratios and comment count which further aids in devising suitable marketing strategies.

Overall,this informative dataset serves as an invaluable asset for researchers,data analysts,and marketers alike who strive to gain deeper understanding about trending video patterns,relevant metrics influencing content virality,factors dictating viewer sentiments,and exploring new possibilities within digital marketing space leveraging YouTube's wide reach

How to use the dataset

How to Use This Dataset: A Guide

In this guide, we will walk you through the different columns in the dataset and provide insights on how you can explore the popularity and engagement of these trending videos. Let's dive in!

Column Descriptions:

• title: The title of the video.
• channel_title: The title of the YouTube channel that published the video.
• publish_date: The date when the video was published on YouTube.
• time_frame: The duration of time (e.g., 1 day, 6 hours) that the video has been trending on YouTube.
• published_day_of_week: The day of week (e.g., Monday) when the video was published.
• publish_country: The country where the video was published.
• tags: The tags or keywords associated with the video.
• views: The number of views received by a particular video
• likes: Number o likes received per each videos
• dislike: Number dislikes receives per an individual vidoe 11.comment_count: number of comments

Popular Video Insights:

To gain insights into popular videos based on this dataset, you can focus your analysis using these columns:

title, channel_title, publish_date, time_frame, and** publish_country**.

By analyzing these attributes together with other engagement metrics such as views ,likes,**dislikes,**comments),comment_count you can identify trends in what type content is most popular both globally or within specific countries.

For instance: - You could analyze which channels are consistently publishing trending videos - Explore whether certain types of titles or tags are more likely to attract views and engagement. - Determine if certain days of the week or time frames have a higher likelihood of trending videos being published.

Engagement Insights:

To explore user engagement with the trending videos, you can focus your analysis on these columns:

likes, dislikes, comment_count

By analyzing these attributes you can get insights into how users are interacting with the content. For example: - You could compare the like and dislike ratios to identify positively received videos versus those that are more controversial. - Analyze comment counts to understand how users are engaging with the content and whether comments being disabled affects overall

Research Ideas

• Analyzing the popularity and engagement of trending videos: By analyzing the number of views, likes, dislikes, and comments, we can understand which types of videos are popular among YouTube users. We can also examine factors such as comment count and ratings disabled to see how viewers engage with trending videos.
• Understanding video trends across different countries: By examining the publish country column, we can compare the popularity of trending videos in different countries. This can help content creators or marketers understand regional preferences and tailor their content strategy accordingly.
• Studying the impact of video attributes on engagement: By exploring the relationship between video attributes (such as title, tags, publish day) and engagement metrics (views, likes), we can identify patterns or trends that influence a video's success on YouTube. This information can be...

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content

Title – Name of the song/music video.

Fulltitle – Complete YouTube video title.

Description – Video description provided by the uploader.

View_count – Total number of views on YouTube.

Categories – Video category (e.g., Music).

Tags – Keywords/tags associated with the video.

Duration – Length of the video in seconds.

Duration_string – Duration in minutes and seconds (formatted).

Live_status – Indicates if the video is live or not.

Thumbnail – URL of the video thumbnail.

Channel – Name of the YouTube channel.

Channel_url – Link to the official channel.

Channel_follower_count – Number of subscribers of the channel.

context

YouTube has become one of the most influential platforms for music consumption worldwide, with millions of users streaming songs and videos daily. In 2025, the platform continues to play a major role in shaping global music trends and artist popularity. This dataset highlights the Top 100 most viewed songs on YouTube in 2025, capturing valuable insights into what listeners are engaging with the most. It reflects how audiences respond to different artists, genres, and music styles, making it a powerful resource for exploring digital music trends, audience behavior, and online engagement patterns.

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This dataset provides a snapshot of YouTube's global reach, presenting user statistics and penetration rates by country primarily for the year 2024, with comparative figures from 2023. It includes the estimated number of unique monthly users (in millions) and the calculated penetration rate (as a percentage of the population) for dozens of countries across the globe.

Context:

The data highlights key markets like India, the United States, Brazil, Indonesia, and Mexico, which lead in total user numbers. It also sheds light on countries with high penetration rates, such as Bahrain and Qatar, indicating widespread adoption within their populations. The dataset is based on aggregated statistics reflecting YouTube's position as a leading global video-sharing platform, owned by Alphabet (Google), with billions of active users worldwide.

Inspiration:

This dataset was created to offer a clear, comparative view of YouTube's user base across different countries, enabling analysis of market size, growth trends (comparing 2024 to 2023), and digital media adoption rates globally. It can be valuable for market researchers, digital marketers, analysts, and anyone interested in the global distribution of online media consumption.

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 October 2025, India was the country with the largest YouTube audience by far, with approximately 500 million users engaging with the popular social video platform. The United States followed, with 254 million YouTube viewers. Indonesia came in third, with 151 million users watching content on YouTube. The United Kingdom saw 55.5 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? Saudi Arabia was the country with the highest YouTube penetration worldwide, as nearly 96 percent of the country's digital population engaged with the service. In 2025, YouTube counted 125 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets YouTube is among the most popular social media platforms worldwide. In terms of in-app (IAP) revenue, the YouTube app generated approximately 53 million U.S. dollars in the United States in December 2024, as well as 17 million U.S. dollars in Japan.

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

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**Trending on YouTube ** Trending helps viewers see what’s happening on YouTube and in the world. Trending aims to surface videos and shorts that a wide range of viewers would find interesting. Some trends are predictable, like a new song from a popular artist or a new movie trailer. Others are surprising, like a viral video.

Trending isn't personalized and displays the same list of trending videos to all viewers in the same country, which is why you may see videos in Trending that aren’t in the same language as your browser. However, in India, Trending displays a list of results for each of the 9 most common Indic languages.

SOURCE The data has been scrapped from "Mendeley.com". The source of this file ishttps://data.mendeley.com/datasets/7pkbvjtnxm/1/files/e7763107-45e9-4613-8c81-146e6a272266 Converted the data to csv file to use it in kaggle ../input/youtube-vdos/youtube trending videos dataset.csv

The data contains following columns . * ) Position (int type) - An index column which gives the position of the channel in youtube channel 1) Channel Id ( Stirng ) - ID of the youtube channel 2) Channel Title ( String ) - Youtube channel title 3) Video Id (String) - ID of video in the youtube channel 4) Published At (String) - date of the video published at 5) Video Title (String ) - Title of the video 6) Video Description (String) - Description of the video(what the video is about) 6 Video Category Id ( int type) - Category of the video in youtube channel 7 Video Category Label (String) - type of category the video belongs
8 Duration (String ) - duration of the video 9 Duration Sec ( int type) - Duration of video in seconds 10 Dimension (String) - Dimension of the video (2D , Hd) 11 Definition (String) - Defining the video 12 Caption (bool ) - Boolean type caption (True or False) 13 Licensed Content (float Type) 14 View Count ( int type) - number of people viewed the video
15 Like Count (float) - Number of likes the channel got 16 Dislike Count (float) - Number of dislikes the channel got 17 Favorite Count ( int type) - Number of people marked as favourite 18 Comment Count (float) - Number of people commented on the video

Comprehensive YouTube channel statistics for 123 GO! TRENDS, featuring 2,550,000 subscribers and 704,574,932 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 US. Track 1,351 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.

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.

The number of Youtube users in India 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 ****** 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 *** 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.

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Top 50 latest trending videos on YouTube across 113 countries. With daily updates, this dataset provides comprehensive information about the top trending videos, including daily rankings, movement trends, view counts, likes, comments, and more

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

My Other Datasets:

Top Spotify Songs in 73 Countries

Wages by Education in the USA

Amazon Products Dataset 2023 (1.4M Products)

Full TMDB Movies Dataset 2023

Interesting Task Ideas:

1. Identify the most popular video categories based on daily trending rankings.
2. Analyze daily video movements to uncover sudden spikes or drops in popularity.
3. Discover which countries have the most consistent or volatile trending videos.
4. Track the performance of specific YouTube channels across different countries.
5. Analyze engagement metrics such as likes, comments, and view counts to determine audience preferences.
6. Analyze the movement trends of videos to identify the most resilient and consistently trending content
7. Visualize the distribution of video languages across different countries.
8. Do sentiment analyisis on video titles to see what emotions generate the most views

Photo by Alexander Shatov on Unsplash

Comprehensive YouTube channel statistics for Up Trends, featuring 133,000 subscribers and 510,939 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. Track 655 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.

🔍 Description: This dataset provides a rich collection of metadata from 2,000+ YouTube videos, offering a unique opportunity to explore how content performs on the world’s largest video-sharing platform. With detailed information on video titles, views, likes, tags, durations, publishing dates, and more, this dataset allows you to dive deep into the world of digital content trends.

Whether you're a data analyst, a machine learning enthusiast, or a content creator, this dataset opens the door to powerful insights about what drives engagement on YouTube.

📌 Key Features: 🎥 Video Title, Channel, and Tags — Understand how content is labeled and branded.

👍 Likes, Dislikes, Comments — Measure audience sentiment and engagement.

⏱️ Duration & Publish Time — See how timing affects performance.

📈 Views — Track popularity and potential virality.

📃 Description Text — Analyze the role of metadata and SEO.

🧠 Great for NLP, sentiment analysis, and predictive modeling tasks.

💡 Potential Uses: Predict video popularity based on title, tags, and other features.

Natural Language Processing (NLP) on video titles and descriptions.

Trend analysis on content type vs. engagement.

Time-series forecasting of views/likes/comments.

Clustering or recommendation systems for video categorization.

YouTube Video and Channel Analysis

YouTube Video and Channel Statistics

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains valuable information about YouTube videos and channels, including various metrics related to views, likes, dislikes, comments, and other related statistics. The dataset consists of 9 direct features and 13 indirect features. The direct features include the ratio of comments on a video to the number of views on the video (comments/views), the total number of subscribers of the channel (subscriberCount), the ratio of likes on a video to the number of subscribers of the channel (likes/subscriber), the total number of views on the channel (channelViewCount), and several other informative ratios such as views/elapsedtime, totalviews/channelelapsedtime, comments/subscriber, views/subscribers, dislikes/subscriber.

The dataset also includes indirect features that are derived from YouTube's API. These indirect features provide additional insights into videos and channels by considering factors such as dislikes/views ratio, channelCommentCount (total number of comments on the channel), likes/dislikes ratio, totviews/totsubs ratio (total views on a video to total subscribers of a channel), and more.

The objective behind analyzing this dataset is to establish statistical relationships between videos and channels within YouTube. Furthermore, this analysis aims to form a topic tree based on these statistical relations.

For further exploration or utilization purposes beyond this dataset description document itself, you can refer to relevant repositories such as the GitHub repository associated with this dataset where you might find useful resources that complement or expand upon what is available in this dataset.

Overall,this comprehensive collection provides diverse insights into YouTube video and channel metadata for conducting statistical analyses in order to better understand viewer engagement patterns varies parameters across different channels. With its range from basic counts like subscriber counts,counting no.of viewership per minute , timing vs viewership rate ,text related user responses etc.,this detailed Youtube Dataset will assist in making informed decisions regarding channel optimization,more effective targeting and creation of content that will appeal to the target audience

How to use the dataset

This dataset provides valuable information about YouTube videos and their corresponding channels. With this data, you can perform statistical analysis to gain insights into various aspects of YouTube video and channel performance. Here is a guide on how to effectively use this dataset for your analysis:

• Understanding the Columns:
  • totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
  • channelViewCount: The total number of views on the channel.
  • likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
  • views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
  • subscriberCount: The total number of subscribers of the channel.
  • dislikes/views: The ratio

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the various ratios and metrics in this dataset, such as comments/views, likes/subscriber, and views/subscribers, one can build predictive models to estimate the popularity or engagement level of YouTube videos. This can help content creators or businesses understand which types of videos are likely to be successful and tailor their content accordingly.
• Analyzing channel performance: The dataset provides information about the total number of views on a channel (channelViewCount), the number of subscribers (subscriberCount), and other related statistics. By examining metrics like views/elapsedtime and totalviews/channelelapsedtime, one can assess how well a channel is performing over time. This analysis can help content creators identify trends or patterns in their viewership and make informed decisions about their video strategies.
• Understanding audience engagement: Ratios like comments/subscriber, likes/dislikes, dislikes/subscriber provide insights into how engaged a channel's subscribers are with its content. By examining these ratios across multiple videos or channels, one can identify trends in audience behavior and preferences. For example, a high ratio of comments/subscriber may indicate strong community participation and active discussion around the videos posted by a particular YouTuber or channel

Acknowledgements

If you use this dataset in y...