I mapped my Twitter followers and this is what I found

I don't love social media, but the power of social networks cannot be underestimated.

Social network analysis can help us understand how people connect and how everything spread around the world, from ideas to diseases.

I could bore you with the details about social analysis and why it's so cool, but instead, I'm going to tell you what I did for my own Twitter network.

My social accounts

This is what my main social media accounts look like:

Social Media Last Interaction Followers
Facebook Deactivated account (years ago) ?
Twitter Reactivated last week - Made a retweet 54
Instagram Reactivated last week - Made a comment 522
LinkedIn Reactivated last week - Made a post 2'378

With only one account above a thousand followers, I'm definitely not an influencer.

There's however an advantage to having a small social following: it becomes very easy to analyze that network. So that's what I did. I selected the social network where I'm the least active and started coding.

The experiment

The first step in my process was defining what I wanted to study. And that was my followers.

So I applied for the Twitter API, and a few e-mails later, I got "Elevated Access" to their API.

You need "elevated access" to get information about the followers of any account, even if they are your own. While this is total bullshit, Twitter is probably the developer friendliest of them all.

The next step was to write some Python code to download my followers and then to download my followers' followers. Doing all this while trying to respect their API limits, making the process simple, but painfully slow.

The follower map

After downloading the data, I cleaned it up a bit, converted it to a spreadsheet format, and imported it into Gephi, an open-source network analysis, and visualization tool.

After playing around with different layouts, this is the result!

A map of my Twitter followers. Network analysis does make for some pretty charts, doesn't it?
Isn't it beautiful?

Even though I'm not active on this social media, I was able to identify some interesting clusters:

Network cluster analysis and respective colors

These are the semi-supervised clusters and their colors:

Cluster Color
Portuguese friends and colleagues Dark Blue
NFTs (Ticklish, Scottwernerd, and Polys.art) Lime
Jacques and his impressive network Green
Enlightenment.ai - my company Orange
International friends (Singapore, London, etc) Purple
Trading and Investing - Grim and Kvark Dark Red
Not really sure Black
Hypnosis accounts (??) Cyan

There are overlaps, of course, e.g. Portuguese friends living abroad, international friends living in Portugal, university colleagues who follow my company, and so on; but this is still a great visualization if I may say so!

Real Network Size

Turns out that even with just my 54 followers, this network of 1st and 2nd-degree followers is made of 11'129 nodes and 13'524 relationships.

This really shows the exponential power of social networks.

Part 2?

It could be interesting to make a Part 2 of this article.

Perhaps, I'll study the limits of my network, and maybe even expand the study to third-degree connections (if Twitter allows me...).

In the meantime, feel free to follow me on Twitter or Instagram - you might show up on the next visualization :)

Definitely not on LinkedIn though, they make it so difficult to use their API, I doubt even the pope is able to access it.

Subscribe to Manuel Levi

Don’t miss out on the latest issues. Sign up now to get access to the library of members-only issues.