User Identity Linkage in Social Media Using Linguistic and Social Interaction Features
This addresses the issue of users evading platform measures by creating multiple accounts, which is crucial for social media platforms and law enforcement to combat illegal content.
The paper tackles the problem of linking multiple social media accounts belonging to the same person to prevent abusive activities, proposing a machine learning model that uses linguistic and social interaction features, and demonstrates its efficacy on Twitter data related to abuse and terrorism.
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.