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.

In 2024, a significant share of 96 percent of Gen Z internet users in the United States watched digital videos. The share of online Gen Z video viewers is expected to witness a marginal increase by 2027, reaching approximately 96.8 percent of the total internet users in this demographic group. As of July 2023, the most popular social media activity among Gen Z users in the U.S. was watching short videos.

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 recorded the highest category reach, with almost half of internet users worldwide reporting to watch music videos online each week. Social video engagement In recent years, 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 28.4 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 55 and 35 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 58 percent of respondents reporting using these devices. In comparison, 12 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.

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!

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†.

The YouTube Insights dataset offers valuable data for researchers, data scientists, and YouTube enthusiasts to explore video performance and engagement. This dataset focuses on key elements such as video titles, view counts, analytics, and subtitles.

With a wide range of YouTube videos, spanning various genres and upload dates, this dataset provides insights into video popularity and audience engagement. Researchers can analyze video titles to understand effective strategies for capturing viewer attention. View counts offer quantitative measures of video popularity, while analytics data provides metrics like likes, dislikes, comments, and shares.

The inclusion of subtitles enhances the dataset, enabling language pattern analysis, sentiment analysis, and keyword extraction. Researchers can uncover correlations between subtitles and video content to gain a deeper understanding of audience preferences and behavior.

The YouTube Insights dataset empowers users to discover valuable insights into YouTube's ecosystem, optimizing content creation and engagement strategies. It serves as a foundation for research, analysis, and innovation in the realm of online video platforms.

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.

According to a survey conducted among consumers worldwide, respondents watched an average of 19 hours of online video content per week in 2022. This represented an increase from 2021, when consumers watched 19 hours of online video content weekly.

Embark on a journey through the fascinating realm of YouTube trending videos with our latest project! Leveraging a comprehensive dataset, we delve into the intricate dynamics behind what makes a video trend on the world's largest video-sharing platform.

Our dataset encapsulates an array of essential features including video_id, trending_date, title, location, channel_title, category_id, publish_time, tags, views, likes, dislikes, comment_count, thumbnail_link, comments_disabled, ratings_disabled, video_error, description, and sheild. With this treasure trove of information at our disposal, we uncover hidden patterns, explore correlations, and extract valuable insights to decode the secrets of YouTube's trending algorithm.

Join us as we employ advanced data analysis techniques to unravel the mysteries behind viral content creation, audience engagement, and the ever-evolving landscape of online video trends. Whether you're a data enthusiast, content creator, or simply curious about the dynamics of digital media, this project offers a captivating exploration into the heart of YouTube's trending phenomenon.

Unlock the power of data and embark on a journey of discovery with our YouTube Trending Video Data Analysis project today!

YouTubeTrending

DataAnalysis

KaggleDataset

VideoInsights

DigitalMedia

ContentCreation

AudienceEngagement

TrendAnalysis

DataScience

OnlineTrends

This dataset pulls daily stats about the Mr.Beast channel youtube video channels including the view count, comment count, and like counts. You can see how these numbers change over time going back from when the dataset was created.

The data is run daily and the pull_date shows stats for each date the data was pulled. This allows you to track video stats over time.

Find the latest video marketing statistics, covering statistics on video content marketing, user trends and habits, B2B video marketing and more.

According to a survey conducted in March 2024 among users in the United States, YouTube was the most popular free service for watching online video content. Viewers aged between 35 and 54 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.

The dataset is first introduced in the following paper: Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. Beyond Views: Measuring and Predicting Engagement in Online Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2018. Tweeted videos dataset This dataset contains YouTube videos published between July 1st and August 31st, 2016. To be collected, the video needs (a) be mentioned on Twitter during aforementioned collection period; (b) have insight statistics available; (c) have at least 100 views within the first 30 days after upload. Quality videos datasets These datasets contain videos deemed of high quality by domain experts. Vevo videos: Videos of verified Vevo artists, as of August 31st, 2016. Billboard16 videos: Videos of 2016 Billboard Hot 100 chart. Top news videos: Videos of top 100 most viewed News channels. freebase_mid_type_name.csv It maps a freebase mid to a real-world entity. See more details in this data description.

As of the second quarter of 2025, ***************** saw the highest reach of online learning videos worldwide, with approximately over ** percent of internet users reporting they watched how-to videos, tutorials, and educational videos online weekly. Internet users located in ************ followed, with around ** percent of respondents stating that they watched learning videos.

As of the second quarter of 2025, 96.5 percent of male internet users worldwide aged between 16 and 24 reported watching online videos every week. Approximately 96.4 percent of women on in the same age group reported watching online videos. Overall, men were more likely to consume online video content than women globally.

As of the second quarter of 2025, Ghana recorded the highest reach of online videos worldwide, with virtually 100 percent of its online population watching video content on a weekly basis. Kenya followed in second place, with 99.8 percent of internet users based in the region reporting to have watched online videos. Approximately 94.6 percent of the global internet audience watched video content online during the last examined quarter.

In March 2022, video consumption via connected TV (CTV) among users in the United States increased by 26.5 percent compared to the previous year, reaching 10.5 billion hours of viewership per month. Online video consumption via mobile experienced a small increase compared to March 2021, reaching 6.8 billion hours watched on monthly basis.

During the second quarter of 2025, 94.6 percent of internet users worldwide watched online videos on a monthly basis. Between the beginning of 2022 and the end of 2023, online video viewership remained constant among the global digital population. During the third quarter of 2022, approximately 92.8 percent of global users were reported watching online videos.

As of June 2025, 96.7 percent of internet users in China watched online videos. Meanwhile, the penetration rate of short video applications was 95.1 percent.

In the third quarter of 2024, **** percent of the surveyed internet users in Turkey watched music videos on a weekly basis. Tutorials and how-to videos followed as the second-most watched online video content category, with **** percent.