Recent events have made it clear that some kinds of technical texts, generated by machine and essentially meaningless, can be confused with authentic, technical texts written by humans. We identify this as a potential problem, since no existing systems for, say the web, can or do discriminate on this basis. We believe that there are subtle, short- and longrange word or even string co-occurrences extant in human texts, but not in many classes of computer generated texts, that can be used to discriminate based on meaning. In this paper we employ the universal lossless source coding algorithms to generate features in a high-dimensional space and then apply support vector machines to discriminate between the classes of authentic and inauthentic texts. Compression profiles for the two kinds of text are distinct— the authentic texts being bounded by various classes of more compressible or less compressible texts that are computer generated. This in turn led to the high prediction accuracy of our models which support our conjecture that there exists a relationship between meaning and compressibility. Our results show that the learning algorithm based upon the compression profile outperformed standard term-frequency text categorization schemes on several non-trivial classes of inauthentic texts.