  Incidents of organized cybercrime are rising because of criminals are reaping
high financial rewards while incurring low costs to commit crime. As the
digital landscape broadens to accommodate more internet-enabled devices and
technologies like social media, more cybercriminals who are not native English
speakers are invading cyberspace to cash in on quick exploits. In this paper we
evaluate the performance of three machine learning classifiers in detecting 419
scams in a bilingual Nigerian cybercriminal community. We use three popular
classifiers in text processing namely: Na\"ive Bayes, k-nearest neighbors (IBK)
and Support Vector Machines (SVM). The preliminary results on a real world
dataset reveal the SVM significantly outperforms Na\"ive Bayes and IBK at 95%
confidence level.
