User Identity Linkage in Social Media Using Linguistic and Social Interaction Features

TL;DR

This paper presents a machine learning model that uses linguistic and social interaction features to link multiple social media accounts to the same user, helping to combat abuse and illegal activities.

Contribution

It introduces a novel detection model leveraging multiple user activity attributes for identity linkage, validated on abusive and terrorism-related Twitter content.

Findings

Effective in identifying accounts of the same user

Demonstrates high accuracy on abusive content cases

Applicable to terrorism-related social media monitoring

Abstract

Social media users often hold several accounts in their effort to multiply the spread of their thoughts, ideas, and viewpoints. In the particular case of objectionable content, users tend to create multiple accounts to bypass the combating measures enforced by social media platforms and thus retain their online identity even if some of their accounts are suspended. User identity linkage aims to reveal social media accounts likely to belong to the same natural person so as to prevent the spread of abusive/illegal activities. To this end, this work proposes a machine learning-based detection model, which uses multiple attributes of users' online activity in order to identify whether two or more virtual identities belong to the same real natural person. The models efficacy is demonstrated on two cases on abusive and terrorism-related Twitter content.