research-article

On the Accuracy of Hyper-local Geotagging of Social Media Content

Authors:

Yana Volkovich,

Yaron KanzaAuthors Info & Claims

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

Pages 127 - 136

https://doi.org/10.1145/2684822.2685296

Published: 02 February 2015 Publication History

Abstract

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data-driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.

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Cited By

Kanza YKrishnamurthy BSrivastava D(2024)A Geospatial Perspective on Data Ownership, the Right to be Forgotten, Copyrights, and Plagiarism in Generative AIProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691269(477-480)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691269
Serere HResch B(2024)Understanding the impact of geotagging on location inference models for accurate generalization to non-geotagged datasetsGeomatica10.1016/j.geomat.2024.10000476:1(100004)Online publication date: Jul-2024
https://doi.org/10.1016/j.geomat.2024.100004
Alsaqer MAlelyani SMohana MAlreemy KAlqahtani A(2023)Predicting Location of Tweets Using Machine Learning ApproachesApplied Sciences10.3390/app1305302513:5(3025)Online publication date: 26-Feb-2023
https://doi.org/10.3390/app13053025
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Index Terms

On the Accuracy of Hyper-local Geotagging of Social Media Content
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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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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National Science Foundation

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WSDM 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

Kanza YKrishnamurthy BSrivastava D(2024)A Geospatial Perspective on Data Ownership, the Right to be Forgotten, Copyrights, and Plagiarism in Generative AIProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691269(477-480)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691269
Serere HResch B(2024)Understanding the impact of geotagging on location inference models for accurate generalization to non-geotagged datasetsGeomatica10.1016/j.geomat.2024.10000476:1(100004)Online publication date: Jul-2024
https://doi.org/10.1016/j.geomat.2024.100004
Alsaqer MAlelyani SMohana MAlreemy KAlqahtani A(2023)Predicting Location of Tweets Using Machine Learning ApproachesApplied Sciences10.3390/app1305302513:5(3025)Online publication date: 26-Feb-2023
https://doi.org/10.3390/app13053025
Khan AZhang HBoudjellal NAhmad AKhan M(2023)LocBERT: Improving Social Media User Location Prediction Using Fine-Tuned BERTDatabase and Expert Systems Applications - DEXA 2023 Workshops10.1007/978-3-031-39689-2_3(23-32)Online publication date: 21-Aug-2023
https://doi.org/10.1007/978-3-031-39689-2_3
Meng CCui YHe QSu LGao J(2022)Towards the Inference of Travel Purpose with Heterogeneous Urban DataIEEE Transactions on Big Data10.1109/TBDATA.2019.29218238:1(166-177)Online publication date: 1-Feb-2022
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TR MSinha D(2022)Twitter Location Prediction usnig Machine Learning Algorithms2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10060688(1066-1070)Online publication date: 18-Nov-2022
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Shukla AChoudhury TBenara NGarg PTiwari AUm J(2022)Geo-based recommendation system utilising geo tagging and K-means clusteringSpatial Information Research10.1007/s41324-022-00495-w31:3(253-263)Online publication date: 16-Nov-2022
https://doi.org/10.1007/s41324-022-00495-w
Ujang UAzri S(2022)Individual mobility pattern in Malaysia during COVID‐19 Recovery Movement Control Order partial lockdownGeo: Geography and Environment10.1002/geo2.1139:1Online publication date: 20-Jun-2022
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Tang SChen Z(2021)Understanding Natural Disaster Scenes from Mobile Images Using Deep LearningApplied Sciences10.3390/app1109395211:9(3952)Online publication date: 27-Apr-2021
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Kravi EKanza YKimelfeld BReichart R(2021)Location Classification Based on TweetsProceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3486635.3491075(51-60)Online publication date: 2-Nov-2021
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