Introduction

Online Video Platform Statistics: An Online Video Platform (OVP) is a crucial digital infrastructure for hosting, managing, and delivering video content online.

It facilitates content uploading, organization, and playback across various devices with adaptive streaming capabilities.

OVPs support monetization through advertising, subscriptions, or pay-per-view models alongside robust analytics for tracking viewer engagement and performance metrics.

They offer customization options for branding and player interfaces, ensuring a seamless user experience. Security features like encryption and DRM safeguard content, while integration with other platforms and APIs enables extended functionality and automation.

OVPs also cater to live streaming needs, making them versatile tools for media, entertainment, education, and corporate sectors seeking reliable video distribution solutions.

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

Video has become one of the most popular online formats, spanning from educational content to product reviews. As of the second quarter of 2025, music videos were the most watched video content type, followed by comedy or viral videos. Social video engagement YouTube and TikTok have become two of the most important social media platforms for global users, as video content commands high levels of engagement. In 2024, users worldwide spent approximately **** hours using the YouTube mobile app per month. Additionally, the leading hashtags used by content creators on TikTok have amassed billions of views: as of October 2025, the TikTok hashtags “fyp” or “for you page” had reached ** and ** billion post views, respectively. Watching content: what device do users prefer? In 2024, televisions were the most used devices for global viewers to watch video-on-demand (VOD), with ** percent of respondents reporting using these devices. In comparison, ** percent of respondents reported using smartphones. Age group and generation are factors impacting viewership habits and device preferences, as younger users appear to prefer using their smartphones to consume content. According to a March 2024 survey, U.S. users aged 18-34 years were more likely to watch video content on smartphones than on any other devices. By comparison, connected TVs were particularly popular for the online video audience aged 35 and older.

Introduction

Online Video Consumption Statistics: Video is now the top choice for content. With 93% of marketers using video in their overall marketing plans, the role of video in marketing has grown significantly in recent years. Social media companies have also boosted this trend by focusing on tools for creating video content.

If you need more clarification about investing in video marketing, this article gathers the latest trends from various studies. Video marketing gives marketers many ways to grow their business and promote their brands. This article will shed more light on "Online Video Consumption Statistics†.

Trending YouTube Video Statistics for unique Video IDs with its Subscriber data

Context

Original dataset was adopted from below URL : Trending YouTube Video Statistics That data was collected for several countries : US(United states of America), GB(Great Britain), DE(Germany), CA(Canada), and FR(France). I chose the data for USA country only. I modified the dataset to analyze some hidden information. Such as, I removed duplicate video_id's and make use of them to retrieve some meaningful data. I removed some unrelated attributes.As per my requirement,I changed type/class of few attributes too. I derived some new attributes from existing once.And many other minor modifications.

All the modifications are done by R-Programming.

I also added a new feature called "subscriber" to the dataset. I collected all subscriber information from youtube.com ,process was automatically done by a python script,written by me.

Content

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.

This dataset is the daily record from the top trending YouTube videos. Top 200 trending videos of a given day.

Original Data was collected during 14th November 2017 & 5th March 2018(though, data for January 10th & 11th of 2017 is missing) Original dataset was collected by Youtube API.

Subscriber column data scrapped by me on 13th March of 2018, through a automated python script. NA introduced in the column for videos those are removed by the Youtube due to copyright or other issue.

Acknowledgements

https://www.kaggle.com/datasnaek/

Inspiration

Analyzing what factors affect how popular a YouTube video will be.

UPDATED: https://www.kaggle.com/datasnaek/youtube-new

SOURCE: https://github.com/DataSnaek/Trending-YouTube-Scraper

Plan

Data collected from the (up to) 200 listed trending YouTube videos every day in the US and the UK.

Description

The dataset includes data gathered from videos on YouTube that are contained within the trending category each day.

There are two kinds of data files, one includes comments and one includes video statistics. They are linked by the unique video_id field.

The headers in the video file are:

• video_id (Common id field to both comment and video csv files)
• title
• channel_title
• category_id (Can be looked up using the included JSON files, but varies per region so use the appropriate JSON file for the CSV file's country)
• tags (Separated by | character, [none] is displayed if there are no tags)
• views
• likes
• dislikes
• thumbnail_link
• date (Formatted like so: [day].[month])

The headers in the comments file are:

• video_id (Common id field to both comment and video csv files)
• comment_text
• likes
• replies

Extra info: The YouTube API is not effective at formatting comments by relevance, although it claims to do so. As a result, the most relevant comments do not align with the top comments at all, they aren't even sorted by likes or replies.

Inspiration

Possible uses for this dataset could include:

• Sentiment analysis in a variety of forms
• Categorising YouTube videos based on their comments and statistics.
• Training ML algorithms to generate their own YouTube comments.
• Analysing what factors affect how popular a YouTube video will be.

Although there are likely many more possibilities, including analysis of changes over time etc.

YouTube keeps track of the most popular videos that are being seen on the site. Several months' worth of daily trending YouTube video statistics are included in this data set. Data for France and the USA are included. The videos on this list are those that users have liked and have received the most views, comments, and likes from other users. These videos are then displayed on the trending page. The greatest videos are shown at the top of the page by ranking these videos according to a ratio of views, likes, comments, and shares.

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

content: Data about daily trending YouTube videos for several months, and counting, is included in this dataset. Up to 200 trending videos are published each day, with data for the US and FR regions (the USA and France, respectively) included.

According to a survey conducted in June 2025 among users in the United States, YouTube was the most popular free service for watching online video content. Viewers aged between 18 and 34 years had the highest YouTube video consumption rate, as 78 percent of them reported using it to watch online videos at least once a week. Facebook ranked second, with 52 percent of viewers aged between 35 and 54 years old reporting using the popular social media platform to watch online content. Instagram and TikTok, which host almost exclusively short-form videos, followed. Instagram was more popular among users aged between 18 and 34, while TikTok was also more popular among younger users.

In 2021, data on global digital video viewership revealed that in 2020 there were over ******billion internet users of any age who watched streaming or downloaded video via any device at least once per month. This figure is projected to increase annually and reach nearly *** billion by the year 2023.

UPDATE: Source code used for collecting this data released here

Context

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.

Content

This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the US, GB, DE, CA, and FR regions (USA, Great Britain, Germany, Canada, and France, respectively), with up to 200 listed trending videos per day.

EDIT: Now includes data from RU, MX, KR, JP and IN regions (Russia, Mexico, South Korea, Japan and India respectively) over the same time period.

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 five 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.

Inspiration

Possible uses for this dataset could include:

• Sentiment analysis in a variety of forms
• Categorising YouTube videos based on their comments and statistics.
• Training ML algorithms like RNNs to generate their own YouTube comments.
• Analysing what factors affect how popular a YouTube video will be.
• Statistical analysis over time.

For further inspiration, see the kernels on this dataset!

The share of internet users who watch online video content from sharing services in Sweden amounted to ***** percent in 2024. Between 2016 and 2024, the share rose by **** percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend.

According to a survey of users in the United States, ***percent of internet users created video content to upload on social media platforms or the web as of June 2025. More than **** of the responding users aged between 18 and 34 years reported having created video content for the internet at least once, while only ** percent of users aged between 35 and 54 years reported doing the same.

MrBeast Youtube Video Stats

MrBeast is one of the biggest youtubers ever. His videos are some of the most viewed of all time and he has perfected the art of gaining views.

This dataset was created using youtube's official api and shows the date created, view count, comments, and upvote counts for all of MrBeast's videos as of December 20, 2021.

Trending video data from YouTube.

Video Object Properties

This dataset represents each trending video object with the following properties:

• title: The title of the video.
• description: The video's description.
• publishedDate: The date and time when the video was published (in a machine-readable format).
• publishedText: The date and time when the video was published (in a human-readable format).
• videoId: The unique identifier for the video.
• videoUrl: The URL of the video.
• channelName: The name of the channel that published the video.
• channelId: The unique identifier for the channel.
• channelUrl: The URL of the channel.
• thumbnails: URLs for the video's thumbnail images in different resolutions.
• views: The number of views the video has received.
• viewsText: The number of views in a human-readable format (e.g., "1.2M views").
• duration: The duration of the video (in a machine-readable format).
• durationText: The duration of the video in a human-readable format (e.g., "3:24").
• verified: A boolean indicating whether the channel is verified or not.
• creatorOnRise: A boolean indicating whether the channel is marked as a "Creator on the Rise" by YouTube.
• isShort: A boolean indicating whether the video is a YouTube Shorts video or not.

trending video data is collected from various categories on YouTube:

• Default: The main YouTube trending page, featuring videos across all categories.
• Music: The trending page specifically for music videos and music-related content.
• Gaming: The trending page for gaming videos, live streams, and gaming-related content.
• Movies: The trending page for movie trailers, clips, and other movie-related videos.

The collected data is saved daily in compressed CSV (Comma-Separated Values) files, with one file per category. The file naming convention follows the format: category_**timestamp**.csv.gz

Where:

• category is the name of the category (e.g., default, music, gaming, movies).
• timestamp is the current date and time in the format YYYYMMDD (e.g., 20230501 for May 1, 2023).

This dataset provides comprehensive statistics on the most popular videos from MrBeast's YouTube channel. It includes information on the video's position in terms of popularity, title, number of views, upload date, video duration, thumbnail image URL, and direct video link. This dataset is ideal for analyzing trends in video performance, content popularity, and engagement metrics over time. Whether you're a data scientist, marketer, or YouTube enthusiast, this dataset offers valuable insights into the factors contributing to the success of one of the platform's biggest creators.

The IACC.3 dataset is approximately 4600 Internet Archive videos (144 GB, 600 h) with Creative Commons licenses in MPEG-4/H.264 format with duration ranging from 6.5 min to 9.5 min and a mean duration of almost 7.8 min. Most videos will have some metadata provided by the donor available e.g., title, keywords, and description.

The share of internet users who watch online video content from sharing services in Norway stood at ***** percent in 2024. Between 2016 and 2024, the share rose by **** percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend.

UPDATE: Source code used for collecting this data released here

Context 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.

Content This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the US, GB, DE, CA, and FR regions (USA, Great Britain, Germany, Canada, and France, respectively), with up to 200 listed trending videos per day.

EDIT: Now includes data from RU, MX, KR, JP and IN regions (Russia, Mexico, South Korea, Japan and India respectively) over the same time period.

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 five 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.

Inspiration Possible uses for this dataset could include:

Sentiment analysis in a variety of forms Categorising YouTube videos based on their comments and statistics. Training ML algorithms like RNNs to generate their own YouTube comments. Analysing what factors affect how popular a YouTube video will be. Statistical analysis over time. For further inspiration, see the kernels on this dataset!

Content

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

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Comprehensive YouTube channel statistics for Learn Online Video, featuring 1,660,000 subscribers and 70,424,121 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 GB. Track 185 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 contains statistics for a selection of YouTube videos, capturing metrics such as views, comments, likes, dislikes, and the timestamp when the data was recorded. The dataset provides insights into the popularity and engagement levels of these videos as of April 15, 2019. This data can be useful for analyzing trends in video performance, user engagement, and the impact of content over time.

File Description: This CSV file contains detailed statistics for a set of YouTube videos, including unique video identifiers and various engagement metrics. Each row represents a different video, and the columns provide specific data points related to the video's performance.

Column Descriptions

videostatsid: Unique identifier for each video statistics entry. ytvideoid: Unique YouTube video identifier. views: The total number of views the video has received. comments: The total number of comments posted on the video. likes: The total number of likes the video has received. dislikes: The total number of dislikes the video has received. timestamp:The date and time when the statistics were recorded, in the format YYYY-MM-DD HH:MM