Topic Partition of User-Generated Texts for User Identity Linkage Across Social Networks
Xiaoyu Guo, Yan Qiao Liu, Fenlin Liu · 2024
User identity linkage across social networks aims to discover the potential correspondence between users across different social platforms. In this paper, we work towards linking users’ identities on diverse social networks by exploring user-generated texts. However, it is non-trivial to solve the problem due to the following challenges. 1) The existing methods rely on massive high-quality anchor links, but it is factually much too expensive or even impossible to acquire supervision information. 2) Users will express unique insights and personalized views on events with different topics in social activities, how to describe users under different topics presents a crucial challenge. Towards this end, we propose an unsupervised method (TPLink) for user identity linkage across social networks based on the topic partition of user-generated texts. Its core idea is that the semantic features exhibited in user-generated texts under different topics differ, but users’ viewpoints and attitudes towards a certain topic will not change with different platforms. Specifically, we first divide user-generated texts according to their topics and learn the topic-specific user representation to depict users at a fine-grained level. Subsequently, we adopt the topic distribution awareness-based similarity measurement to mine the correspondence between users across different networks. Through extensive experiments on the Instagram-Twitter dataset, we demonstrate that the proposed TPLink method significantly outperforms the state-of-the-art methods. The ablation studies further illustrate the rationality and effectiveness of our method.