User Identity Linkage on Social Networks: A Review of Modern Techniques and Applications

In an Online Social Network (OSN), users can create a unique public persona by crafting a user identity that may encompass profile details, content, and network-related information. As a result, a relevant task of interest is related to the ability to link identities across different OSNs that can have multiple implications in several contexts both at the individual level (e.g., better knowledge of users) and at the group level (e.g., predicting network dynamics, information diffusion, etc.). The purpose of this work is to provide a comprehensive review of recent studies (from 2016 to the present) on User Identity Linkage (UIL) methods across online social networks. It would offer guidance for other researchers in the field by outlining the main problem formulations, the different feature extraction strategies, algorithms, machine learning models, datasets, and evaluation metrics proposed by researchers working in this area. To this aim, the proposed overview takes a pragmatic perspective to highlight the concrete possibilities for accomplishing this task depending on the type of available data. Our analysis demonstrates significant progress in addressing the UIL task, largely due to the development of more advanced deep-learning architectures. Nevertheless, certain challenges persist, primarily stemming from the limited availability of benchmark datasets. This limitation is further compounded by current social network access policies, which prioritize privacy protection and reduce opportunities to retrieve data through APIs.