  There is an ever growing number of users with accounts on multiple social
media and networking sites. Consequently, there is increasing interest in
matching user accounts and profiles across different social networks in order
to create aggregate profiles of users. In this paper, we present models for
Digital Stylometry, which is a method for matching users through stylometry
inspired techniques. We experimented with linguistic, temporal, and combined
temporal-linguistic models for matching user accounts, using standard and novel
techniques. Using publicly available data, our best model, a combined
temporal-linguistic one, was able to correctly match the accounts of 31% of
5,612 distinct users across Twitter and Facebook.
