Abstract
Identifying users across social networks has received more and more attention in recent years. In this paper we propose a model that combines ATM and DTM. The model uses the ATM model to study the user’s potential behavior information and extract user’s topic preferences. Then it analyzes the trend of user topic preferences changing with time through the DTM. The purpose of this paper is to improve the accuracy of account identification across social networks. The experimental results show that the model performs well on Chinese text or mixed text in Chinese and English because it obtains higher accuracy and F1 scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tang, J., Chen, W.: In-depth analysis and mining for large social data. Chin. Sci. Bull. 509–519 (2015)
Kai, S., Wang, S., Tang, J., Zafarani, R., Liu, H.: User identity linkage across online social networks: a review. ACM SIGKDD Explor. Newsl. 18, 5–17 (2017)
Nie, Y., Yan, J., Li, S., Xiang, Z., Li, A., Zhou, B.: Identifying users across social networks based on dynamic core interests. Neurocomputing 210, 107–115 (2016)
Almishari, M., Tsudik, G.: Exploring linkability of user reviews. In: Foresti, S., Yung, M., Martinelli, F. (eds.) ESORICS 2012. LNCS, vol. 7459, pp. 307–324. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33167-1_18
Sha, Y., Liang, Q., Zheng, K.: Matching user accounts across social networks based on users message. Procedia Comput. Sci. 80, 2423–2427 (2016)
Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: HYDRA: large-scale social identity linkage via heterogeneous behavior modeling (2014)
Rosen-Zvi, M., Griffiths, T.L., Steyvers, M., Smyth, P.: The author-topic model for authors and documents (2012)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of International Conference on Machine Learning, pp. 113–120 (2006)
Wu, Z., Yu, H., Liu, S., Zhu, Y.: User identification across multiple social networks based on information entropy. J. Comput. Appl. 37, 2374–2380 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, S., Qiao, H. (2019). Matching User Accounts Across Social Networks Based on LDA Model. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)