Abstract
Cross social network user identification aims to identify the same entity on various online social networks to enhance the completeness and accuracy of the persona. There are three broad categories of cross-social network user identification methods: user identification on account of basic user information, user identification on the basis of network topology graphs, and user identification based on the user's origin. This paper analyzes users’ display names from different social networks to determine whether they are the same person. The process consists of three steps: first, we obtain information about users and bring their display names from social networking sites. Secondly, we analyze the user's name, get a series of values from the user's name through similarity calculation methods, and match the similarity. We perform similarity matching on the real dataset by using some classification models. Our model performs well, with F1 values reaching 97.07%, 94.65%, and 92.05% for the three datasets, respectively. This paper can provide a high-quality dataset for downstream NLP tasks of high research significance and value.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, Z., Lin, D., Li, P. (2023). Across Online Social Network User Identification Based on Usernames. In: Jiang, X. (eds) Machine Learning and Intelligent Communication. MLICOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 481. Springer, Cham. https://doi.org/10.1007/978-3-031-30237-4_11
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DOI: https://doi.org/10.1007/978-3-031-30237-4_11
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