Large-scale micro-blog authorship attribution: Beyond simple feature engineering

Abstract

With the ever-growing use of social media, authorship attribution plays an important role in avoiding cybercrime, and helping the analysis of online trails left behind by cyber pranks, stalkers, bullies, identity thieves and alike. In this paper, we propose a method for authorship attribution in micro-blogs with efficiency one hundred to a thousand times faster than state-of-the-art counterparts. The method relies on a powerful and scalable feature representation approach taking advantage of user patterns in micro-blog messages, and also on a custom-tailored pattern classifier adapted to deal with big data and high-dimensional data. Finally, we discuss search-space reduction when analyzing hundreds of online suspects and millions of online micro messages, which makes this approach invaluable for digital forensics and law enforcement.