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.

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...

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...

Overview

This curated collection encompasses 598 of the most engaging and widely viewed YouTube videos from the early months of 2024, capturing a diverse snapshot of digital culture and trends. This dataset not only reflects the dynamic landscape of online content but also serves as a lens through which to explore the intricacies of viewer engagement and content popularity on a global scale.

Data Science Applications

Despite its modest size, this dataset offers a rich playground for data scientists and researchers. It is particularly suited for:

• Sentiment Analysis: Uncover the emotional undertones within video titles, potentially correlating them with viewer engagement metrics.
• Trend Analysis: Identify patterns and trends in video popularity, including temporal spikes in viewer interest.
• Predictive Modeling: Use engagement metrics to predict future trends or the potential virality of content types.
• Content Strategy Insights: Derive actionable insights for content creators and marketers aiming to enhance viewer engagement.

Column Descriptors

The dataset comprises the following columns, each offering unique insights:

• Title: The video's title, providing a glimpse into the content and themes.
• Published At: Timestamp indicating the video's release date, useful for temporal analysis.
• Duration: The length of the video, offering context on content depth and viewer commitment.
• View Count: The total number of views, a direct metric of popularity and reach.
• Like Count: Reflecting viewer approval and engagement.
• Comment Count: Indicative of viewer interaction and discussion sparked by the video.

Ethically Mined Data

This dataset has been ethically mined, adhering to privacy standards and YouTube's data use policies. Identifiable personal information has been excluded to ensure privacy and ethical compliance.

Acknowledgments

We extend our gratitude to YouTube for fostering an open platform that serves as a rich source of digital culture and public sentiment. This dataset would not have been possible without the vast array of content shared by creators and the engagement from viewers worldwide.

Thumbnail Image Credit

The thumbnail image for this dataset has been generated using DALL-E 3, an advanced AI model known for creating compelling and relevant visual content.

YouTube Music Hits Dataset

A collection of YouTube music video data sourced from Wikidata, focusing on videos with significant viewership metrics.

  Dataset Description





  Overview

24,329 music videos View range: 1M to 5.5B views Temporal range: 1977-2024

  Features

youtubeId: YouTube video identifier itemLabel: Video/song title performerLabel: Artist/band name youtubeViews: View count year: Release year genreLabel: Musical genre(s)

  View… See the full description on the dataset page: https://huggingface.co/datasets/akbargherbal/youtube-music-hits.

Comprehensive ranking dataset of the top 100 YouTube channels worldwide. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 452,000,000 subscribers and 101,598,825,577 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

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

Context

This dataset represents real performance metrics of content on a YouTube channel. Each row seems to correspond to an individual video or campaign, with various KPIs (Key Performance Indicators) like views, watch time, reach, CTR, engagement, and earnings. The dataset can be used for YouTube performance analysis, influencer marketing evaluation, or content strategy optimization.

Name
Date : Date of video publishing or performance snapshot Video Title : Title or name of the video content Platform : Social platform, likely YouTube Influencer Name : Name of the content creator/influencer Subscribers : Total subscriber count at the time Views : Total video views Watch Time (Hours) : Total watch time in hours Average View Duration : Average watch duration per viewer Reach : Unique accounts reached Click Through Rate (%) : CTR for the video thumbnail Likes : Number of likes received Comments : Number of comments Shares : Number of shares Revenue : Revenue or earnings from video (AdSense or influencer payout)

Use Cases

Performance monitoring of YouTube content

ROI and ROAS analysis for influencer campaigns

A/B testing for video titles/thumbnails

Trend identification across different video types

Comprehensive ranking dataset of the top 100 YouTube channels from United Kingdom. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 42,400,000 subscribers and 32,645,209,430 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

This dataset encompasses mobile app based media consumption, collected from over 150,000 first-party US Daily Active Users on Android devices. Use it for measurement, journey understanding or to trigger surveys about sentiment. Platforms covered include Netflix, YouTube, Disney+ and Amazon Prime Video.

Fields include pre-roll ads played, viewing duration, channel, category and more. All data tied to demographics, all consumers can be surveyed about viewership (or other topics), and consumer journey understanding can be gleaned combining this dataset with other MFour OmniTraffic® products.

Comprehensive ranking dataset of the top 500 YouTube channels worldwide. This dataset features 500 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 452,000,000 subscribers and 101,598,825,577 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

News dissemination plays a vital role in supporting people to incorporate beneficial actions during public health emergencies, thereby significantly reducing the adverse influences of events. Based on big data from YouTube, this research study takes the declaration of COVID-19 National Public Health Emergency (PHE) as the event impact and employs a DiD model to investigate the effect of PHE on the news dissemination strength of relevant videos. The study findings indicate that the views, comments, and likes on relevant videos significantly increased during the COVID-19 public health emergency. Moreover, the public’s response to PHE has been rapid, with the highest growth in comments and views on videos observed within the first week of the public health emergency, followed by a gradual decline and returning to normal levels within four weeks. In addition, during the COVID-19 public health emergency, in the context of different types of media, lifestyle bloggers, local media, and institutional media demonstrated higher growth in the news dissemination strength of relevant videos as compared to news & political bloggers, foreign media, and personal media, respectively. Further, the audience attracted by related news tends to display a certain level of stickiness, therefore this audience may subscribe to these channels during public health emergencies, which confirms the incentive mechanisms of social media platforms to foster relevant news dissemination during public health emergencies. The proposed findings provide essential insights into effective news dissemination in potential future public health events.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Comprehensive ranking dataset of the top 100 YouTube channels from Germany. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 36,000,000 subscribers and 47,579,063,650 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

This dataset contains detailed analytics of the YouTube channel of NDTV News, a leading news television and digital journalism organization in India. The data was collected through web scraping and includes the following columns:

1. Title: The title of each video on NDTV's YouTube channel
2. Subscriber Count: The number of subscribers of the NDTV YouTube channel at the time the data was collected.
3. Views: The number of views each video has received on the NDTV YouTube channel.
4. Likes: The number of likes each video has received on the NDTV YouTube channel.
5. Summary Caption: A brief summary or caption for each video.
6. Date: The date when each video was uploaded to the NDTV YouTube channel.

This dataset provides valuable insights into the viewership and popularity of NDTV's digital content, as well as the overall growth of their YouTube channel. It can be used for various data analysis and research purposes such as trend analysis, video performance comparison, and more.

Comprehensive ranking dataset of the top 100 YouTube channels from Philippines. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 54,300,000 subscribers and 58,060,828,521 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

**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 Politricks Watch, featuring 408,000 subscribers and 196,322,123 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 GB. Track 1,961 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.

As of February 2025, it was found that around 14.1 percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately 16.6 percent of the platform's audience. The online audience of the popular social video platform was further composed of 14.6 percent of female users aged between 25 and 34 years, and 20.7 percent of male users in the same age group.

Comprehensive YouTube channel statistics for JD Sports, featuring 577,000 subscribers and 354,560,411 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. Track 1,467 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.