Christel Geurts graduates on Cross-Domain Authorship Attribution

Cross-Domain Authorship Attribution as a Tool for Digital Investigations

by Christel Geurts

On the darkweb sites promoting illegal content are abundant and new sites are constantly created. At the same time Law Enforcement is working hard to take these sites down and track down the persons involved. Often, after taking down a site, users change their name and move to a different site. But what if Law Enforcement could track users across sites? Different sites or sources of information are called a domain. As the domain changes, often the context of a message also changes, making it challenging to track users simply on words used. The aim of this thesis is to develop a system that can link written text of authors in a cross-domain setting. The system was tested on a blog corpus and verified on police data. Tests show that multinomial logistic regression and Support Vector Machines with a linear kernel perform well. Character 3-grams work well as features, combining multiple feature sets increases performance. Tests show that Logistic Regression models with a combined feature set performed best (accuracy = 0.717, MRR = 0.7785, 1000 authors (blog corpus)). On the police data the Logistic Regression model had an accuracy of 0.612 and a MRR of 0.6883 for 521 authors.

Comments are closed.