This visualization aims to help you to publish the next trending video on YouTube. We provide essential information on how to optimize your title and publishing time for each video category and country.
The dataset used in the following visualizations is a daily record of the top trending YouTube videos. It proposes numerous insights such as title, channel name, category, views, likes, dislikes, comments, publish time, tags, etc. from different regions of the planet (USA, Great Britain, Germany, Canada, France, Russia, Mexico, South Korea, Japan and India). It is provided by Kaggle and offers information for more than 200k top trending videos.
Note how in the UK an average trending video has almost 1.3 millions visualizations whereas in Russia, a video gets to trending with as low as 150 thousands views.
Another interesting insight is given by the views per category distribution in each nation. We can see that music is the predominant category with more thatn 55% of all the views, instead Indian producers are more active in the entratainment category.
Percentage of views in each category:
The color indicates the average number ov views per top trending video
The circular packing plot shows tags used more than 1000 times sorted by their category color
The circular bar plot shows the 51 most used tags of each category
Category:Take a look at how the average length of the title varies among categories.
See how titles in the "sports" category are on average shorter than in the "cars and vehicles" category
Category:Note that videos tend to be published more in the afternoon/evening and end of the week. There are also months where publishers seem to be more active.
The color indicates the number of views of videos published at a given weekday and hour
The following heatmap shows when videos are published during the day and in each month. We can see that some months collect more activity.
Note that the number of views in months going from August to November are really low. This is due to the lack of data regarding those months in the dataset.
The color indicates the number of views of videos published at a given month and hour
EPFL, CS 480 - Pablo Pfister - Luis de Lima Carvalho - Stanislas Furrer