I wanted to do this exploration because I have noticed some posts get a huge reach with very few shares/likes/comments. I have also noticed some posts have a large number of shares/likes/comments but reach a small number of people.
I did principal component analysis on Facebook data from our last 2,000 posts (4/16/2015 – 7/7/2015). Principal components can reveal structure to data which you cannot see otherwise. PCA refresher here: http://setosa.io/ev/principal-component-analysis/
There are four variables – number of likes, number of comments, number of shares, and reach. Reach is the number of unique people who saw the post.
I broke down the data by type – link, photo, status update, or video.
Below are 3 combinations of principal components. The most striking observation (to me) is that video posts (purple) can get a huge reach with very few shares/likes/comments. However, just being a video post does not guarantee a large reach.
I know this is pretty well-known about Facebook videos. It is neat, however, to see it in the data structure.
Code for the ggplot2 biplot: https://github.com/vqv/ggbiplot/blob/master/R/ggbiplot.r