I'm a frequent user of Twitter, but I realize that among the major social networks it could be the hardest to get into. One of the big obstacles for me was that, as I followed more and more people representing my different interests, my timeline became an overcrowded mess with too many different types of content. For example, at one point I started following many tech-related accounts and comic book art-related accounts at the same time, and when I would go on Twitter I could never reasonably choose to consume content from only one of the groups.
Even after learning to adapt to this, I still thought that it would be nice to be able to detect distinct groups among the twitter accounts that I followed. The impetus to finally start a project about this came when I started using cluster analysis algorithms in my machine learning class - the algorithms used seemed to be exactly the right idea for this kind of community detection. With that I set off on the task to collect and analyze the data from my own Twitter follow list, with clusters!
The work I've done since then is still in progress (mostly because the results I'm getting aren't that great yet), and as I make more progress I'll be making more posts about it!
All the code is available on Github.
More details below!