research-article

You Are Where You Go: Inferring Demographic Attributes from Location Check-ins

Authors:

Nicholas Jing Yuan,

Xing XieAuthors Info & Claims

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Pages 295 - 304

https://doi.org/10.1145/2684822.2685287

Published: 02 February 2015 Publication History

Abstract

User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' "Likes" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes.

We collect a large dataset consisting of profiles of 159,530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location check-ins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC.

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Cited By

Zhang JZhang XHong DGupta RShang J(2025)Contextual Inference From Sparse Shopping Transactions Based on Motif PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.345263837:2(572-583)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3452638
Zhang YBan X(2025)Demographic Information Inference from Passively Collected DataJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.TEENG-8628151:3Online publication date: Mar-2025
https://doi.org/10.1061/JTEPBS.TEENG-8628
Liu GSun XLi HGuo ZLi YPi S(2025)Knowledge-enhanced heterogeneous graph attention networks for privacy co-disclosure detection in online social networkExpert Systems with Applications10.1016/j.eswa.2024.126266268(126266)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126266
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Index Terms

You Are Where You Go: Inferring Demographic Attributes from Location Check-ins
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

February 2015

482 pages

ISBN:9781450333177

DOI:10.1145/2684822

General Chairs:
Xueqi Cheng
ICT, Chinese Academy of Sciences, China
,
Hang Li
Huawei Technologies, China
,
Program Chairs:
Evgeniy Gabrilovich
Google, USA
,
Jie Tang
Tsinghua University, China

Copyright © 2015 ACM.

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Published: 02 February 2015

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WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining

February 2 - 6, 2015

Shanghai, China

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WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhang JZhang XHong DGupta RShang J(2025)Contextual Inference From Sparse Shopping Transactions Based on Motif PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.345263837:2(572-583)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3452638
Zhang YBan X(2025)Demographic Information Inference from Passively Collected DataJournal of Transportation Engineering, Part A: Systems10.1061/JTEPBS.TEENG-8628151:3Online publication date: Mar-2025
https://doi.org/10.1061/JTEPBS.TEENG-8628
Liu GSun XLi HGuo ZLi YPi S(2025)Knowledge-enhanced heterogeneous graph attention networks for privacy co-disclosure detection in online social networkExpert Systems with Applications10.1016/j.eswa.2024.126266268(126266)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126266
Zhao QHe YWang YLi WWu LQiu W(2024)Investigating the Civic Emotion Dynamics during the COVID-19 Lockdown: Evidence from Social MediaSSRN Electronic Journal10.2139/ssrn.4782864Online publication date: 2024
https://doi.org/10.2139/ssrn.4782864
Liu LFu SHuang XLuo YZhang XChoo K(2024)SecDM: A Secure and Lossless Human Mobility Prediction SystemIEEE Transactions on Services Computing10.1109/TSC.2024.335829217:4(1793-1805)Online publication date: Jul-2024
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Ma WWang DChen CWen SFei GXiang Y(2024)LocGuard: A Location Privacy Defender for Image SharingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337692921:6(5526-5537)Online publication date: Nov-2024
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Qiu GTang GLi CLuo LGuo DShen Y(2024)A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication ServicesIEEE Internet of Things Journal10.1109/JIOT.2023.330747811:3(5467-5490)Online publication date: 1-Feb-2024
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Liu YWang SWang XZheng YChen XXu YKang C(2024)Towards semantic enrichment for spatial interactionsAnnals of GIS10.1080/19475683.2024.232439230:2(151-166)Online publication date: 6-Mar-2024
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Zhang BRasouli SFeng T(2024)Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approachTravel Behaviour and Society10.1016/j.tbs.2024.10084337(100843)Online publication date: Oct-2024
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Zufferey NHumbert MTavenard RHuguenin KCalandrino JTroncoso C(2023)Watch your watchProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620249(193-210)Online publication date: 9-Aug-2023
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