This paper presents a meaning-based method to distinguish text without or with little semantic content from text that has meaning which can be processed. The basic method assumes that a semantic analyzer will be able to produce less output from semantically less grammatical input text. The method was pilot-tested on a corpus of blog spam. Future improvements, including a method to distinguish semantically unified from semantically disparate text are sketched. The tested method, but even more the projected improvements, open up the way to taking the spam filtering arms race to a new level that is very costly to spam producers.