This is my first try at creating a map of lemmy. I based it on the overlap of commentors that visited certain communities.
I only used communities that were on the top 35 active instances for the past month and limited the comments to go back to a maximum of August 1 2024 (sometimes shorter if I got an invalid response.)
I scaled it so it was based on percentage of comments made by a commentor in that community.
Here is the code for the crawler and data that was used to make the map:
Well I used dimensionality reduction to make it 2D so the axes are how the algorithm chose to compress it.
The original data had each data point as a community and the features as a frequency of a user posting in that community.
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Huh, interesting. So is the idea to spread the data out as much an possible, while keeping “similar” communities near each other? What was the dimensionality of the original set?
Total communities: 2986
Total users: 21934
So the dimensions were reduced from (2986, 21934) to (2986, 2)
Edit: Also yeah it is using Umap for the algorithm and it does do something pretty similar to what you described.
Anti Commercial-AI license (CC BY-NC-SA 4.0)
That’s really interesting! It shows which communities share users. I am part of jlai.lu, a french-speaking community that is relatively isolated by also slrpnk.net that seems very spread out!
Would it make sense to compute the standard deviation of each instance’s communities? It would give an idea of which are islands and which are more extended. Not sure if it makes sense to compute it more on 2 dimensions or on the original 21934 though.
Yeah that sounds like a good idea so you can see how connected local communities are. Probably makes more sense to use original dimensions so no extra information is lost.
Anti Commercial-AI license (CC BY-NC-SA 4.0)