Toptal

This is the final installment in a three-part series on Twitter cluster analyses using R and Gephi. Part one analyzed heated online discussion about famed Argentine footballer Lionel Messi; part two deepened the analysis to better identify principal actors and understand topic spread.

Politics are polarizing. When we find interesting communities with drastically different opinions, Twitter messages generated from within these camps tend to densely cluster around two groups of users, with a slight connection between them. This type of grouping and relationship is called homophily: the tendency to interact with those similar to us.

In the previous article in this series, we focused on computational techniques based on Twitter data sets and were able to generate informative visualizations through Gephi. Now we want to use cluster analysis to understand the conclusions we can draw from those techniques and identify which social data aspects are most informative.

We will change

To read the full article click on the 'post' link at the top.