A Multiple Salient Features-Based User Identification across Social Media

Autor: Yating Qu, Huahong Ma, Honghai Wu, Kun Zhang, Kaikai Deng
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Entropy, Vol 24, Iss 4, p 495 (2022)
Druh dokumentu: article
ISSN: 24040495
1099-4300
DOI: 10.3390/e24040495
Popis: Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users’ different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).
Databáze: Directory of Open Access Journals
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