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A large-scale empirical study of geotagging behavior on Twitter

Authors:

Kathleen M. CarleyAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 365 - 373

https://doi.org/10.1145/3341161.3342870

Published: 15 January 2020 Publication History

Abstract

Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.

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Cited By

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https://doi.org/10.1177/23998083241278275
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
Dubey SMeijers MSmit ESmit E(2024)Beyond climate change? Environmental discourse on the planetary boundaries in Twitter networksClimatic Change10.1007/s10584-024-03729-y177:5Online publication date: 25-Apr-2024
https://doi.org/10.1007/s10584-024-03729-y
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A large-scale empirical study of geotagging behavior on Twitter
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 15 January 2020

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Knoblauch SGroß SLautenbach Sde Aragão Rocha AGonzález MResch BArifi DJänisch TMorales IZipf A(2024)Long-term validation of inner-urban mobility metrics derived from Twitter/XEnvironment and Planning B: Urban Analytics and City Science10.1177/23998083241278275Online publication date: 14-Oct-2024
https://doi.org/10.1177/23998083241278275
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
Dubey SMeijers MSmit ESmit E(2024)Beyond climate change? Environmental discourse on the planetary boundaries in Twitter networksClimatic Change10.1007/s10584-024-03729-y177:5Online publication date: 25-Apr-2024
https://doi.org/10.1007/s10584-024-03729-y
Koshy RElango S(2024)Applying social media in emergency response: an attention-based bidirectional deep learning system for location reference recognition in disaster tweetsApplied Intelligence10.1007/s10489-024-05462-654:7(5768-5793)Online publication date: 27-Apr-2024
https://doi.org/10.1007/s10489-024-05462-6
Owuor IHochmair H(2023)Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet MentionsGeographies10.3390/geographies30300313:3(584-609)Online publication date: 16-Sep-2023
https://doi.org/10.3390/geographies3030031
van der Lijn CEhnström EKoivisto SMuukkonen P(2023)Detecting informal green, blue, and street physical activity spaces in the city using geotagged sports-related Twitter tweetsFrontiers in Sociology10.3389/fsoc.2023.11253438Online publication date: 5-May-2023
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Serere HResch BHavas C(2023)Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selectionPLOS ONE10.1371/journal.pone.028294218:3(e0282942)Online publication date: 15-Mar-2023
https://doi.org/10.1371/journal.pone.0282942
Jiang JThomason JBarbieri FFerrara EHorvát ÁHall WContractor NFröhling LOgnayova KTaneja HWeber IGligori? KMejova Y(2023)Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on TwitterProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583596(136-146)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3578503.3583596
Jiang RWang ZTao YYang CSong XShibasaki RChen SShyu M(2023)Learning Social Meta-knowledge for Nowcasting Human Mobility in DisasterProceedings of the ACM Web Conference 202310.1145/3543507.3583991(2655-2665)Online publication date: 30-Apr-2023
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Wu NMu L(2023)Impact of COVID-19 on online grocery shopping discussion and behavior reflected from Google Trends and geotagged tweetsComputational Urban Science10.1007/s43762-023-00083-03:1Online publication date: 22-Feb-2023
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