Digital Stylometry: Linking Profiles Across Social Networks

TL;DR

This paper introduces Digital Stylometry, a method for linking user profiles across social networks by analyzing linguistic and temporal writing patterns, achieving a 31% match rate across Twitter and Facebook.

Contribution

It presents novel models combining linguistic and temporal features for user account matching, demonstrating improved accuracy over existing methods.

Findings

Best model correctly matched 31% of users

Combined temporal-linguistic model outperformed individual models

Utilized publicly available data for evaluation

Abstract

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.