research-article

Geo-social media data analytic for user modeling and location-based services

Authors:

Nicholas Jing YuanAuthors Info & Claims

SIGSPATIAL Special, Volume 7, Issue 3

Pages 11 - 18

https://doi.org/10.1145/2876480.2876484

Published: 11 January 2016 Publication History

Abstract

More and more geo-tagged social media data is generated, nowadays, from the geo-tagged tweets, geo-tagged photos to check-ins. Analyzing this flourish data enables the possibility for us to discover users daily mobility patterns, profiles and preferences. As a result, based on the analyzed results, new types of location-based services emerge. In this article, we first introduce the recent advances in location-based user preferences modeling, which includes: 1) inferring users demographics, 2) identifying users novelty-seeking characteristics and 3) discovering users shopping impulsiveness. After that, we present a comprehensive summary on the state-of-arts of the location-based services, which take advantage of the geo-social media, including: 1) location-based recommendations, 2) location-based predication.

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Cited By

Ahmad M(2023)Leveraging Social Media Geographic Information for Smart Governance and Policy MakingGlobal Perspectives on Social Media Usage Within Governments10.4018/978-1-6684-7450-1.ch013(192-213)Online publication date: 30-Jun-2023
https://doi.org/10.4018/978-1-6684-7450-1.ch013
He TBao JLi YHe HZheng Y(2023)Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313819535:4(3589-3602)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3138195
Recchi ETittel K(2023)The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital DataHandbook of Computational Social Science for Policy10.1007/978-3-031-16624-2_23(437-464)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-16624-2_23
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Geo-social media data analytic for user modeling and location-based services
1. Information systems
  1. Information systems applications

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Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 7, Issue 3

November 2015

38 pages

EISSN:1946-7729

DOI:10.1145/2876480

Editor:
Chi-Yin (Ted) Chow
City University of Hong Kong

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Copyright © 2016 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2016

Published in SIGSPATIAL Volume 7, Issue 3

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Cited By

Ahmad M(2023)Leveraging Social Media Geographic Information for Smart Governance and Policy MakingGlobal Perspectives on Social Media Usage Within Governments10.4018/978-1-6684-7450-1.ch013(192-213)Online publication date: 30-Jun-2023
https://doi.org/10.4018/978-1-6684-7450-1.ch013
He TBao JLi YHe HZheng Y(2023)Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313819535:4(3589-3602)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2021.3138195
Recchi ETittel K(2023)The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital DataHandbook of Computational Social Science for Policy10.1007/978-3-031-16624-2_23(437-464)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-16624-2_23
Zhang LDarzi AGhader SPack MXiong CYang MSun QKabiri AHu S(2021)Interactive COVID-19 Mobility Impact and Social Distancing Analysis PlatformTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812110438132677:4(168-180)Online publication date: 18-Sep-2021
https://doi.org/10.1177/03611981211043813
Matsui AMoriwaki D(2021)Online-to-offline advertisements as field experimentsThe Japanese Economic Review10.1007/s42973-021-00101-yOnline publication date: 18-Oct-2021
https://doi.org/10.1007/s42973-021-00101-y
Thao TKobayashi RYamaguchi R(2020)Effects of Temporal Factors on Human Mobility Lifestyle Through GPS Information2020 7th International Conference on Behavioural and Social Computing (BESC)10.1109/BESC51023.2020.9348333(1-4)Online publication date: 5-Nov-2020
https://doi.org/10.1109/BESC51023.2020.9348333
Xiong CHu SYang MYounes HLuo WGhader SZhang L(2020)Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USAJournal of The Royal Society Interface10.1098/rsif.2020.034417:173(20200344)Online publication date: 16-Dec-2020
https://doi.org/10.1098/rsif.2020.0344
Wen RLi S(2020)A review of the use of geosocial media data in agent-based models for studying urban systemsBig Earth Data10.1080/20964471.2020.1810492(1-19)Online publication date: 10-Sep-2020
https://doi.org/10.1080/20964471.2020.1810492
Zhou LChen XZhao YZheng K(2019)Top-K Spatio-Topic Query on Social Media DataDatabase Systems for Advanced Applications10.1007/978-3-030-18579-4_40(678-693)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-18579-4_40
Rizwan MWan WCervantes OGwiazdzinski L(2018)Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into PlayISPRS International Journal of Geo-Information10.3390/ijgi70501967:5(196)Online publication date: 19-May-2018
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