How to Build a Self-Organising Map (SOM) in Python Step-by-Step

In a separate blog post, I covered the basis for understanding how self-organising maps (SOM) work. This included what a SOM is, why SOM’s are useful and how they work – including the training algorithm. In this blog post, we’ll go through how we can build a simple SOM neural network of our own using … Read more

How to Build Tag Clouds From Mastodon Hashtags

Hashtags are an important part of microblogging and are used to reach a wider audience of interested people. Much like Twitter, hashtags are also widely used on Mastodon and it’s common for users to include as many hashtags as possible in their posts to maximum the audience reach. Also, as shown in a previous post, … Read more

Finding New Followers on Mastodon Using Python and Network Science

I’m still relatively new to Mastodon and, so far, I love it. Ever since Twitter started going down hill (for reasons which I won’t cover yet again) I have used Mastodon as my go-to microblogging platform of choice. I’ve been on Mastodon for a couple of months now, and I’m on the lookout for new … Read more

Modelling Discussion Threads From Mastodon Timelines Using Python and Networkx

On Mastodon, users have the ability to reply to another user’s post by leaving comments as a response. This feature is designed to encourage users to engage and build connections with others. With this function, users can initiate a conversation and participate with others which forms what is known as a discussion thread. From a … Read more

Building User-Hashtag Co-occurrence Network from Timelines on Mastodon

Hashtags are an incredibly useful feature for annotating posts and have an active role on microblogging platforms such as Twitter and Mastodon. They are particularly helpful for discovering new people to follow and finding interesting posts. In a separate post, I used Twitter to build hashtag co-occurrence networks based upon a specific hashtag. The general … Read more