This looks fantastic. In particular, the focus on many-class classification is important, it's a common real-world task that is often overlooked. I have some suggestions:

More types of baseline accuracy measure would be useful, eg. accuracy, and micro and macro f1 with unbalanced classes.

It would be very useful to know inter-annotator agreement for the manual classification and human performance for the task of identifying the original subreddit. I'm not a huge fan of creating artificial categories when natural ones are available. In practice there will be a real difference between the 26th and 27th league of legends subreddit, it might be some subtle topical focus shift or something political or tonal.

Is there some kind of standard measure for trading precision and recall when classifying in a hierarchical class structure? That is, you start predicting general high-level categories and move down to the most specific class you can get to before confidence falls below a threshold? Then the evaluation measure gives you more credit for getting lower down the tree (rewarding information gain in the class hierarchy).