Abstract
User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.
Highlights
With the development of social networks and their diversity, the number of active users on social networks has increased year by year
We apply the following relationship jointly with the location in profile to conduct user identification. e experiments prove that the features extracted in this paper are effective for user identification. e experiments indicate that using multiple user information, we can improve the performance of user identification
To evaluate the effectiveness of the multiple user information user identification framework (MUIUI) framework, we compare MUIUI with three existing methods: the method proposed by Li [11], the OPL method proposed by Zhang [15], and the ALLEN-logistic regression (LR) method proposed by Zhang [40]. e experiments use the dataset introduced in Section 5.1, which has 1292 pairs of anchor users and 1292 pairs of nonanchor users. e dataset includes 1881 Twitter users and 1305 Facebook users
Summary
With the development of social networks and their diversity, the number of active users on social networks has increased year by year. If we can match accounts of an individual in different social networks, we can integrate his more comprehensive personal information and draw out their complete friend relationships [4]. There have been many existing works on user identification across social networks. Most existing works use attributes in the profile to user identification [4, 11,12,13,14,15], such as display name, profile photo, and location. Some existing works are relationship-based user identification [17,18,19]. Taking into user privacy settings and social network restrictions on data crawl, we may only get part of relationships. A number of existing works use UGCs to user identification [4, 20]
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