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.

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.

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.

In 2022, approximately ** percent of Dutch internet users watched content on video sharing platforms online. This represents a decrease from 2020, when approximately ** percent of the digital population of the Netherlands reported watching content on online sharing video websites. In 2022, over eight in ** people in the Netherlands reported watching video content via online sharing platforms, while online social video usage among the country's digital population exceeded ** percent in the last recorded year.

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.

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.

This data set is an every hour real time count observation(views, comments, likes, dislikes ) during May, 2018 of videos that are released on April, 2018 on Youtube. The number of videos are around 1500 and the video list was retrieved on 7th May 2018, which is the starting date of this data set.

My first intention was to observe how statistic counts of random videos change along the time.

There seems to be many insights hidden in the data set waiting to be discovered.

There are some nan in the data set due to my mistake during the period but working with missing data can be also a good practice.

thank you

This data set is retrieved by Youtube API

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.

This statistic shows the results of a survey on traditional television and online video watching habits compared to the previous year in the United Kingdom (UK) in November 2018. The survey found that ** percent of respondents stated that they watched both less traditional TV and more online videos compared to the year before.

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...

CN: Internet Service: No of User: Online Video(Include Short-Form Video) data was reported at 1,070.180 Person mn in Dec 2024. This records an increase from the previous number of 1,067.960 Person mn for Jun 2024. CN: Internet Service: No of User: Online Video(Include Short-Form Video) data is updated semiannually, averaging 974.710 Person mn from Dec 2018 (Median) to Dec 2024, with 13 observations. The data reached an all-time high of 1,070.180 Person mn in Dec 2024 and a record low of 724.860 Person mn in Dec 2018. CN: Internet Service: No of User: Online Video(Include Short-Form Video) data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage.

Comprehensive YouTube channel statistics for Daily Dose Of Internet, featuring 20,700,000 subscribers and 17,079,716,556 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 1,763 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.

CN: Internet Service: No of User: Online Video data was reported at 612.010 Person mn in Dec 2018. This records an increase from the previous number of 609.060 Person mn for Jun 2018. CN: Internet Service: No of User: Online Video data is updated semiannually, averaging 380.220 Person mn from Jun 2007 (Median) to Dec 2018, with 24 observations. The data reached an all-time high of 612.010 Person mn in Dec 2018 and a record low of 98.982 Person mn in Jun 2007. CN: Internet Service: No of User: Online Video data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage.

The statistic presents data on the revenue of leading platforms for viewing gaming video content worldwide in 2018. According to the estimates, Twitch generated approximately *** billion in revenues, accounting for ** percent of gaming video content platform revenues in the measured period. The total revenue for all platforms that year reached *** billion U.S. dollars.

China Internet Service: Usage Rate: Online Video data was reported at 73.900 % in Dec 2018. This records a decrease from the previous number of 76.000 % for Jun 2018. China Internet Service: Usage Rate: Online Video data is updated semiannually, averaging 67.200 % from Dec 2005 (Median) to Dec 2018, with 26 observations. The data reached an all-time high of 76.900 % in Dec 2007 and a record low of 36.300 % in Dec 2006. China Internet Service: Usage Rate: Online Video data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage.

I created this dataset as part of a data analysis project and concluded that it might be relevant for others who are interested in examining in analyzing content on YouTube. This dataset is a collection of over 6000 videos having the columns:

• Channel: video's channel
• Title: video title
• PublishedDate: date the video was uploaded
• Likes: likes count for the video
• Views: views count for the video

• Comments: comments count for the video

  Through the YouTube API and using Python, I collect data about some of these popular channels' videos that provide educational content about Machine Learning and Data Science in order to extract insights about which topics had been popular within the last couple of years. Featured in the dataset are the following creators:

• Krish Naik

• Nicholas Renotte

• Sentdex

• DeepLearningAI

• Artificial Intelligence — All in One

• Siraj Raval

• Jeremy Howard

• Applied AI Course

• Daniel Bourke

• Jeff Heaton

• DeepLearning.TV

• Arxiv Insights

These channels are features in multiple top AI channels to subscribe to lists and have seen a big growth in the last couple of years on YouTube. They all have a creation date since or before 2018.

CN: Internet Service: Usage Rate: Online Video(Include Short-Form Video) data was reported at 96.600 % in Dec 2024. This records a decrease from the previous number of 97.100 % for Jun 2024. CN: Internet Service: Usage Rate: Online Video(Include Short-Form Video) data is updated semiannually, averaging 94.500 % from Dec 2018 (Median) to Dec 2024, with 13 observations. The data reached an all-time high of 97.700 % in Dec 2023 and a record low of 87.500 % in Dec 2018. CN: Internet Service: Usage Rate: Online Video(Include Short-Form Video) data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage.

The statistic shows the penetration rate of internet and online video in Vietnam in 2018, broken down by generation. In that year, the online video penetration among Generation Z was at ** percent while the rate was at ** percent among millenials.

In the third quarter of 2023, approximately ** percent of Vietnamese internet users reported having watched online videos. In the same year, music videos were the most popular type of video to stream online.

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