Digital Stylometry: Linking Profiles Across Social Networks
This addresses the need for linking profiles across social networks to create aggregate user profiles, but it is incremental as it builds on existing stylometry techniques.
The paper tackled the problem of matching user accounts across different social networks by developing Digital Stylometry models, achieving a 31% correct match rate for 5,612 users across Twitter and Facebook.
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.