User Identification in Online Social Networks using Graph Transformer Networks

2021 ◽  
Author(s):  
K. N. Pavan Kumar ◽  
Marina L. Gavrilova
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Identification

User identification across online social networks in practice: Pitfalls and solutions

2016 ◽  
Vol 44 (3) ◽  
pp. 377-391 ◽  
Author(s):  
Azadeh Esfandyari ◽  
Matteo Zignani ◽  
Sabrina Gaito ◽  
Gian Paolo Rossi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Full Range ◽  
Application Programming Interface ◽  
User Identification ◽  
Application Programming ◽  
Test Sets ◽  
Set Up ◽  
Different Levels ◽  
Programming Interface

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.

scholarly journals User Identification Based on Display Names Across Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17342-17353 ◽  
Author(s):  
Yongjun Li ◽  
You Peng ◽  
Wenli Ji ◽  
Zhen Zhang ◽  
Quanqing Xu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Identification

scholarly journals Exploiting Two-Level Information Entropy across Social Networks for User Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ling Xing ◽  
Kaikai Deng ◽  
Honghai Wu ◽  
Ping Xie ◽  
Mingchuan Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Entropy ◽  
Online Social Networks ◽  
User Behavior ◽  
User Profile ◽  
Recall Rate ◽  
Entropy Method ◽  
Stable Marriage ◽  
User Identification ◽  
Level Information

As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

A Study on Influential User Identification in Online Social Networks

2016 ◽  
Vol 25 (3) ◽  
pp. 467-473 ◽  
Author(s):  
Nan Wang ◽  
Yadong Zhou ◽  
Qindong Sun ◽  
Si Shen
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Identification

scholarly journals User Identification Based on Integrating Multiple User Information across Online Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wenjing Zeng ◽  
Rui Tang ◽  
Haizhou Wang ◽  
Xingshu Chen ◽  
Wenxian Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
User Privacy ◽  
User Identification ◽  
Multiple User ◽  
Information User ◽  
User Data ◽  
The Impact

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.

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