YouTube Video and Channel Analytics

YouTube Video and Channel Analytics: Statistics and Features

By VISHWANATH SESHAGIRI [source]

About this dataset

The YouTube Video and Channel Metadata dataset is a comprehensive collection of data related to YouTube videos and channels. It consists of various features and statistics that provide insights into the performance and engagement of videos, as well as the overall popularity and success of channels.

The dataset includes both direct features, such as total views, channel elapsed time, channel ID, video category ID, channel view count, likes per subscriber, dislikes per subscriber, comments per subscriber, and more. Additionally, there are indirect features derived from YouTube's API that provide additional metrics for analysis.

One important aspect covered in this dataset is the ratio between certain metrics. For example:

• The totalviews/channelelapsedtime ratio represents the average number of views a video has received relative to the elapsed time since the channel was created.
• The likes/dislikes ratio indicates the proportion of likes on a video compared to dislikes.
• The views/subscribers ratio showcases how engaged subscribers are by measuring the number of views relative to the number of subscribers.

Other metrics explored in this dataset include comments/views ratio (representing viewer engagement), dislikes/views ratio (measuring viewer sentiment), comments/subscriber ratio (indicating community participation), likes/subscriber ratio (reflecting audience loyalty), dislikes/subscriber ratio (highlighting dissatisfaction levels), total number of subscribers for a channel (subscriberCount), total views on a channel (channelViewCount), total number of comments on a channel (channelCommentCount), among others.

By analyzing these features and statistics within this dataset, researchers or data analysts can gain valuable insights into various aspects related to YouTube videos and channels. Furthermore, it may be possible to build statistical relationships between videos based on their performance characteristics or even develop topic trees based on similarities between different content categories. This dataset serves as an excellent resource for studying YouTube's ecosystem comprehensively.

For accessing additional resources related to this dataset or exploring code repositories associated with it, users can refer to the provided GitHub repository

How to use the dataset

Introduction:

Step 1: Understanding the Dataset
Start by familiarizing yourself with the columns in the dataset. Here are some key features to pay attention to:

• 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 for a channel.
• dislikes/views: The ratio of dislikes on a video to its total views.
• comments/subscriber: The ratio comments on a video receive per subscriber count.

Step 2: Determining Data Analysis Objectives
Define your objectives or research questions before diving into data analysis using this dataset. For example, you may want to explore relationships between viewership, engagement metrics, and various attributes such as category ID or elapsed time.

Step 3: Analyzing Relationships between Variables
Use statistical techniques like correlation analysis or visualization tools like scatter plots, bar graphs, or heatmaps to understand relationships between variables in this dataset.

For example:

• Plotting totalviews/channelelapsedtime against channelViewCount can help identify patterns between overall video popularity and channels' view count growth over time.
• Comparing likes/dislikes with comments/views can give insights into viewer engagement levels across different videos.

Step 4: Building Machine Learning Models (Optional)
If your objective includes predictive analysis or building machine learning models, select relevant features as predictors and the target variable (e.g., totalviews/channelelapsedtime) for training and evaluation.

You can use various algorithms such as linear regression, decision trees, or neural networks to predict video performance or channel growth based on available attributes.

Step 5: Evaluating Model Performance
Assess the predictive model's performance using appropriate evaluation metrics like mean squared error, accuracy, precision, recall, or F1 score. This step helps validate the effectiveness of your developed model for the given objectives.

Conclusion:
The YouTube Video and

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the features and statistics of YouTube videos such as total views, likes per subscriber, comments per subscriber, and dislikes per subscriber, we can build a model to predict the popularity of new videos. This can help content creators optimize their video content and marketing strategies.
• Identifying successful YouTube channels: By examining metrics like total views, channel view count, subscriber count, and likes per subscriber, we can identify successful YouTube channels in different categories. This information can be useful for brands looking for potential collaborations or advertisers looking to target specific audiences.
• Analyzing engagement on YouTube channels: By studying ratios such as comments/views and likes/dislikes, we can gain insights into the level of engagement on different YouTube channels. This information is valuable for content creators who want to understand how their audience is interacting with their videos and make improvements accordingly

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit VISHWANATH SESHAGIRI.