Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2019

Abstract

User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account’s features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a new UIL method which (i) learns and assigns weights to the user features used for user identity linkage and (ii) handles new user features introduced by new user-generated data. We evaluated AD-Link on realworld datasets from three popular online social platforms, namely, Twitter, Facebook and Foursquare. The results show that AD-Link outperforms the state-of-the-art UIL methods.

Keywords

user identity linkage, user data growing, user attribute weight

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

2019 IEEE International Conference on Big Knowledge 9th ICBK: Beijing, November 10-11: Proceedings

First Page

183

Last Page

190

ISBN

9781728146072

Identifier

10.1109/ICBK.2019.00032

Publisher

IEEE

City or Country

Pistacaway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICBK.2019.00032

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