The signal-to-noise ratio in Twitter is already low as it is. Most tweets contain links, lots of users are followers but are not followed.
Sure, combining several clever heuristics can reduce spam levels, and no doubt Twitter must attack this aspect if it wants to stay relevant.
But can you put your finger on what exactly constitutes spam in a platform that is so noisy?
Has anyone tried using a Bayesian filter for twitter spam? These have been very successful for email. In fact, I consider email spam a solved problem now (thanks Gmail!)
It's for things like this that I wish I could insert a proxy between my twitter clients and twitter itself and build my own rules/spam filter.
My assertion is that after a certain point (which we are not far from), Twitter as a platform will have a problem making a distinction between "spam" and "legitimate content".
Where you can see the real problem is when there are trending hashtags that spammers get a hold of... spammers grab hold onto a hashtag and keep it artifically trending long past its relevance.