User Identity Linkage Across Social Media via Attentive Time-Aware User Modeling

In this paper, we work towards linking users' identities on different social media platforms by exploring the user-generated contents (UGCs). This task is non-trivial due to the following challenges. 1) As UGCs involve multiple modalities (e.g., text and image), how to accurately characterize the user account based on their heterogeneous multi-modal UGCs poses the main challenge. 2) As people tend to post similar UGCs on different social media platforms during the same period, how to effectively model the temporal post correlation is a crucial challenge. And 3) no public benchmark dataset is available to support our user identity linkage based on heterogeneous UGCs with timestamps. Towards this end, we present an attentive time-aware user identity linkage scheme, which seamlessly integrates the temporal post correlation modeling and attentive user similarity modeling. To facilitate the evaluation, we create a comprehensive large-scale user identity linkage dataset from two popular social media platforms: Instagram and Twitter. Extensive experiments have been conducted on our dataset and the results verify the effectiveness of the proposed scheme. As a residual product, we have released the dataset, codes, and parameters to facilitate other researchers.