Xiaolin Yao
ABSTRACT
The same user registers on different social platforms which scatters user data across multiple platforms, these data are incomplete, unreliable, and underutilized. Through the analysis of these cross-platform data, identify different accounts corresponding to the same user's real identity. Linking cross-platform user identities can help build detailed user profiles, recommendation systems and link predictions etc. Our research divides user basic attributes into three types: string attributes, short text attributes and semi-structured attributes, according to the characteristics of different attributes, we study the calculation method of similarity between attributes. In order to improve the effect of user identity recognition, this study selects the optimal machine learning algorithm for user characteristics of different dimensions and completes the learning process by constructing and integrating multiple models. Experimental results show that the integration model based on user profile features has a certain degree of user identity link ability, can effectively integrate multiple categories of attributes between platforms, and improve the accuracy of identity link algorithm. This paper provides a reference for the research on user identification across multiple social networks.
- Zhou X, Liang X, Zhang H, Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(2): 411-424.Google Scholar
Digital Library
- Korula N, Lattanzi S. An efficient reconciliation algorithm for social networks[J]. Proceedings of the VLDB Endowment, 2014, 7(5): 377-388.Google ScholarDigital Library
- K. Shu, S. Wang, J. Tang, R. Zafarani, H. Liu, User Identity Linkage across Online Social Networks: A Review. ACM SIGKDD Explorations Newsletter, 2016, 18(2): 5-17.Google ScholarDigital Library
- T. Iofciu, P. Fankhause, F. Abel, K. Bischoff, Identifying users across social tagging systems. Proceedings of the Conference on Weblogs and Social Media, Barcelona, Catalonia, 2010.Google Scholar
- W. Cohen, P. Ravikumar, S. Fienberg, A comparison of string metrics for matching names and records. Proceedings of the Conference on Knowledge Discovery and Data Mining, 2003.Google Scholar
- S. Liu, S. Wang, F. Zhu, J. Zhang, R. Krishnan, HYDRA: large-scale social identity linkage via heterogeneous behavior modeling. Proceedings of the ACM SIGMOD International Conference on Management of Data, Snowbird, Utah, 2014: 22-27.Google Scholar
- Y. Shen, H. Jin, Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014: 381-390.Google Scholar
- [8] A. Malhotra, L. Totti, W. M. Jr, P. Kumaraguru, V. Almeida, Studying user footprints in different online social networks. Proceedings of the Conference on Advances in Social Networks Analysis and Mining, Washington, DC, 2012: 1065-1070.Google Scholar
- J. Vosecky, D. Hong, V. Y. Shen, User identification across multiple social networks. Proceedings of the Conference on Networked Digital Technologies, Ostrava, Czech Republic, 2009.Google Scholar
- M. Motoyama, G. Varghese, I seek you: searching and matching individuals in social networks. Proceedings of the 8th workshop on Web information and data management, Hong Kong, 2009: 67-75.Google Scholar
- N. Korula, S. Lattanzi, An efficient reconciliation algorithm for social networks. Proceedings of the VLDB Endowment, 2014, 7(5): 377-388.Google ScholarDigital Library
- Shu K, Wang S, Tang J, User Identity Linkage across Online Social Networks: A Review[J]. ACM SIGKDD Explorations Newsletter, 2017, 18(2):5-17.Google ScholarDigital Library
- Zhong Zexuan, CaoYong, Guo Mu, CoLink: An Unsupervised Framework for User Identity Linkage [C]. AAAI Conference on Artificial Intelligence. 2018.Google ScholarCross Ref
- Ya-Ru Y, Yong-Qing W, Zhi-Bin Z, Social network user identity linkage model based on comprehensive information[J]. Journal of Shandong University (Natural Science), 2019.Google Scholar
- Hailang Wei, Gui Li, Zhengyu Li, Ziyang Han, Keyan Cao. Semi-Structured Entity Resolution Algorithm[J]. Hans Journal of Data Mining,2020, 10(1), 1-15.Google Scholar
- Ghanbari-Adiv F, Mosleh M. Text Emotion Detection in Social Networks Using a Novel Ensemble Classifier Based on Parzen Tree Estimator (TPE)[J]. Neural Computing and Applications, 2019, (4).Google ScholarCross Ref
Index Terms
- User Identity Linkage Across Multiple Social Network Based on Profile Features
Recommendations
User Profile Linkage Across Multiple Social Platforms
Web Information Systems Engineering – WISE 2020AbstractLinking user profiles belonging to the same people across multiple social networks underlines a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Most of existing approaches focus on ...
User Identity Linkage across Online Social Networks: A Review
The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or ...
UPON: User Profile Transferring across Networks
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementUser profiling has very important applications for many downstream tasks, such as recommender system, behavior prediction and market strategy. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, ...
Comments