short-paper

Locate Who You Are: Matching Geo-location to Text for User Identity Linkage

Authors:

Xueqi ChengAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3413 - 3417

https://doi.org/10.1145/3459637.3482134

Published: 30 October 2021 Publication History

Abstract

Nowadays, users are encouraged to activate across multiple online social networks simultaneously. User identity linkage, which aims to reveal the correspondence among different accounts across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, information from different social platforms may also be asymmetric, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel user identity linkage framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external text-location pairs. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.

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Cited By

Huang HDing FYin HLiu GWang CWu D(2024)EgoMUIL: Enhancing Spatio-Temporal User Identity Linkage in Location-Based Social Networks With Ego-Mo HypergraphIEEE Transactions on Mobile Computing10.1109/TMC.2023.334531223:8(8341-8354)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3345312
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615084(2208-2218)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615084
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CANA: Causal-enhanced Social Network AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614799(2219-2228)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614799
Show More Cited By

Index Terms

Locate Who You Are: Matching Geo-location to Text for User Identity Linkage
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 30 October 2021

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Huang HDing FYin HLiu GWang CWu D(2024)EgoMUIL: Enhancing Spatio-Temporal User Identity Linkage in Location-Based Social Networks With Ego-Mo HypergraphIEEE Transactions on Mobile Computing10.1109/TMC.2023.334531223:8(8341-8354)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2023.3345312
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615084(2208-2218)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615084
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CANA: Causal-enhanced Social Network AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614799(2219-2228)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614799
Kou YLi DShen DNie TYu G(2022)Incremental User Identification Across Social Networks Based on User-Guider Similarity IndexJournal of Computer Science and Technology10.1007/s11390-022-2430-037:5(1086-1104)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11390-022-2430-0

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