Capturing Deep Dynamic Information for Mapping Users across Social Networks

Chiyu Cai, Linjing Li, Weiyun Chen, Daniel Zeng

Abstract: Nowadays, it is common that a netizen creates multiple accounts across social platforms. Mapping accounts across platforms could facilitate various applications in security. Existing methods usually focus on profile and network based features. In this paper, we concentrate on capturing dynamic information of social users and present a deep dynamic user mapping model to identify the accounts across platforms. The proposed model captures dynamic latent features from three aspects including posting pattern, writing pattern, and emotional fluctuation. We also develop a matching network that fuses dynamic and traditional features to identify accounts. To the best knowledge of ourselves, this is the first trial that applies deep neural network in mapping users with dynamic information. Experiments on real world dataset demonstrated the effectiveness of the proposed method.