  Computer generated academic papers have been used to expose a lack of
thorough human review at several computer science conferences. We assess the
problem of classifying such documents. After identifying and evaluating several
quantifiable features of academic papers, we apply methods from machine
learning to build a binary classifier. In tests with two hundred papers, the
resulting classifier correctly labeled papers either as human written or as
computer generated with no false classifications of computer generated papers
as human and a 2% false classification rate for human papers as computer
generated. We believe generalizations of these features are applicable to
similar classification problems. While most current text-based spam detection
techniques focus on the keyword-based classification of email messages, a new
generation of unsolicited computer-generated advertisements masquerade as
legitimate postings in online groups, message boards and social news sites. Our
results show that taking the formatting and contextual clues offered by these
environments into account may be of central importance when selecting features
with which to identify such unwanted postings.
