Because if it is the case, this really suits my need.
I'm trying to discover popular searches on a website. For this, I'm using TopK algorithm which is based on Bloom Filter hashing.
I don't want "Hello world" and "hello world" to be count twice. So if it is a collision, that would be really appropriate for my use case.
Practically, I'm using this implementation.
Practically, I can run String similarity prior to adding to TopK bucket, like for any new string item, if it is similar to at least one String in the bucket, then convert it to the one in the bucket before adding. But this would be stacking same logics (in case it could be achieved directly with Bloom filters).