Word Embedding or Hash?
It depends…

The general rule of thumb is that there should be at least 40 occurrences of an item to train an embedding model to find a robust representation. If most follower IDs repeat then an embedding model can learn which ones co-occur. If follower IDs are sparse then hashing (which randomly assigns numbers) is a better choice.

Which method is better is an empirical question. You can create both models, benchmark, and then choose the data processing pipeline that is best for your task.