research-article

Public Access

The Geography and Importance of Localness in Geotagged Social Media

Authors:

Isaac L. Johnson,

Subhasree Sengupta,

Johannes Schöning,

Brent HechtAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 515 - 526

https://doi.org/10.1145/2858036.2858122

Published: 07 May 2016 Publication History

Abstract

Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is "local", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.

References

[1]

Sofiane Abbar, Yelena Mejova, and Ingmar Weber. 2015. You Tweet What You Eat: Studying Food Consumption Through Twitter. ACM Press. http://doi.org/10.1145/2702123.2702153

Digital Library

[2]

Luc Anselin. 2005. Exploring Spatial Data with GeoDa: A Workbook. Center for Spatially Integrated Social Science.

[3]

Hila Becker, Mor Naaman, and Luis Gravano. 2011. Beyond Trending Topics: Real-World Event Identification on Twitter. ICWSM. http://doi.org/10.1.1.221.2822

[4]

Roger Bivand and Gianfranco Piras. Regional Research Institute.

[5]

Zhiyuan Cheng, James Caverlee, Kyumin Lee, and Daniel Z. Sui. 2011. Exploring Millions of Footprints in Location Sharing Services. ICWSM 2011.

[6]

Ryan Compton, Chi-Kwan Lee, Tsai-Ching Lu, Lakdeepal de Silva, and Michael Macy. 2013. Detecting future social unrest in unprocessed twitter data: "emerging phenomena and big data." ISI, IEEE, 56--60.

[7]

Ryan Compton, Craig Lee, Jiejun Xu, et al. 2013. Using publicly visible social media to build detailed forecasts of civil unrest. 1--11.

[8]

Justin Cranshaw, Jason I Hong, and Norman Sadeh. 2012. The Livehoods Project?: Utilizing Social Media to Understand the Dynamics of a City. ICWSM: 58--65.

[9]

Aron Culotta. 2014. Estimating county health statistics with twitter. JSM Proceedings, ACM Press, 1335--1344. http://doi.org/10.1145/2556288.2557139

Digital Library

[10]

Bertrand De Longueville, Robin S. Smith, and Gianluca Luraschi. 2009. Omg, from here, i can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. Proceedings of the 2009 international workshop on location based social networks, ACM, 73--80.

Digital Library

[11]

Melanie Eckle. Quality Assessment of Remote Mapping in OpenStreetMap for Disaster Management Purposes. Retrieved September 24, 2015 from http://iscram2015.uia.no/wp-content/uploads/2015/05/51.pdf

[12]

Eric Fischer. Locals and Tourists. Retrieved from https://www.flickr.com/photos/walkingsf/sets/72157624 209158632/

[13]

Huiji Gao, Jiliang Tang, and Huan Liu. 2012. Exploring Social-Historical Ties on Location-Based Social Networks. ICWSM.

[14]

Song Gao, Linna Li, Wenwen Li, Krzysztof Janowicz, and Yue Zhang. 2014. Constructing gazetteers from volunteered Big Geo-Data based on Hadoop. Computers, Environment and Urban Systems. http://doi.org/10.1016/j.compenvurbsys.2014.02.004

[15]

S.A. Golder and M.W. Macy. 2011. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures. Science 333, 6051: 1878--1881. http://doi.org/10.1126/science.1202775

[16]

Mark Graham, Ralph K. Straumann, and Bernie Hogan. 2015. Digital Divisions of Labor and Informational Magnetism: Mapping Participation in Wikipedia. Annals of the Association of American Geographers 0, 0: 1--21. http://doi.org/10.1080/00045608.2015.1072791

[17]

Darren Hardy, James Frew, and Michael F. Goodchild. 2012. Volunteered geographic information production as a spatial process. International Journal of Geographical Information Science: 1--22. http://doi.org/10.1080/13658816.2011.629618

Digital Library

[18]

Brent Hecht and Darren Gergle. 2010. On the "localness" of user-generated content. CSCW: 229. http://doi.org/10.1145/1718918.1718962

Digital Library

[19]

Brent Hecht, Lichan Hong, Bongwon Suh, and Ed H. Chi. 2011. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. CHI, ACM.

Digital Library

[20]

Brent Hecht and Monica Stephens. 2014. A Tale of Cities': Urban Biases in Volunteered Geographic Information. ICWSM.

[21]

Nadav Hochman and Raz Schwartz. 2012. Visualizing instagram: Tracing cultural visual rhythms. ICWSMSocMedVis, 6--9.

[22]

Myung-Hwa Hwang, Shaowen Wang, Guofeng Cao, Anand Padmanabhan, and Zhenhua Zhang. 2013. Spatiotemporal transformation of social media geostreams: a case study of Twitter for flu risk analysis. ACM Press, 12--21. http://doi.org/10.1145/2534303.2534310

Digital Library

[23]

David Jurgens, Tyler Finethy, James McCorriston, Yi Tian Xu, and Derek Ruths. 2015. Geolocation prediction in twitter using social networks: A critical analysis and review of current practice. ICWSM.

[24]

Marina Kogan, Leysia Palen, and Kenneth M. Anderson. 2015. Think Local, Retweet Global: Retweeting by the Geographically-Vulnerable during Hurricane Sandy. CSCW, ACM Press, 981--993. http://doi.org/10.1145/2675133.2675218

Digital Library

[25]

Giorgos Kordopatis-Zilos, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2015. Geotagging Social Media Content with a Refined Language Modelling Approach. In Intelligence and Security Informatics, Michael Chau, G. Alan Wang and Hsinchun Chen (eds.). Springer International Publishing, Cham, 21--40.

[26]

Juhi Kulshrestha, Farshad Kooti, Ashkan Nikravesh, and P. Krishna Gummadi. 2012. Geographic Dissection of the Twitter Network. ICWSM.

[27]

Vasileios Lampos and Nello Cristianini. 2010. Tracking the flu pandemic by monitoring the social web. Cognitive Information Processing (CIP), 2010 2nd International Workshop on, IEEE, 411--416.

[28]

Linna Li, Michael F. Goodchild, and Bo Xu. 2013. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science 40, 2: 61--77. http://doi.org/10.1080/15230406.2013.777139

[29]

Momin M. Malik, Hemank Lamba, Constantine Nakos, and Jürgen Pfeffer. 2015. Population Bias in Geotagged Tweets. ICWSM.

[30]

James McCorriston, David Jurgens, and Derek Ruths. 2015. Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter. ICWSM.

[31]

Alan Mislove, Sune Lehmann, Yong-yeol Ahn, Jukkapekka Onnela, and J Niels Rosenquist. Understanding the Demographics of Twitter Users. ICWSM: 554--557.

[32]

Lewis Mitchell, Morgan R Frank, Kameron Decker Harris, Peter Sheridan Dodds, and Christopher M Danforth. 2013. The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS one 8, 5. http://doi.org/10.1371/journal.pone.0064417

[33]

Mohamed Musthag and Deepak Ganesan. 2013. Labor dynamics in a mobile micro-task market. CHI, ACM, 641--650.

Digital Library

[34]

Mor Naaman, Amy Xian Zhang, Samuel Brody, and Gilad Lotan. 2012. On the Study of Diurnal Urban Routines on Twitter. ICWSM: 258--265.

[35]

Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. 2012. Mining user mobility features for next place prediction in location-based services. ICDM, IEEE, 1038--1043.

Digital Library

[36]

Barbara Poblete, Ruth Garcia, Marcelo Mendoza, and Alejandro Jaimes. 2011. Do all birds tweet the same?: characterizing twitter around the world. CIKM, ACM, 1025--1030. http://doi.org/10.1145/2063576.2063724

Digital Library

[37]

Derek Ruths and Jürgen Pfeffer. 2014. Social media for large studies of behavior. Science 346, 6213: 1063--1064.

[38]

Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. WWW, ACM, 851--860.

Digital Library

[39]

Raz Schwartz and Germaine R Halegoua. 2014. The spatial self: Location-based identity performance on social media. New Media & Society: 1--18. http://doi.org/10.1177/1461444814531364

[40]

Shilad Sen, Toby Jia-Jun Li, WikiBrain Team, and Brent Hecht. 2014. WikiBrain: Democratizing Computation on Wikipedia. OpenSym, ACM, 27:1--27:10. http://doi.org/10.1145/2641580.2641615

Digital Library

[41]

Shilad W. Sen, Heather Ford, David R. Musicant, Mark Graham, Oliver S.B. Keyes, and Brent Hecht. 2015. Barriers to the Localness of Volunteered Geographic Information. CHI, ACM Press, 197--206. http://doi.org/10.1145/2702123.2702170

Digital Library

[42]

Alessio Signorini, Alberto Maria Segre, and Philip M. Polgreen. 2011. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLoS ONE 6, 5: e19467. http://doi.org/10.1371/journal.pone.0019467

[43]

Anthony Stefanidis, Andrew Crooks, and Jacek Radzikowski. 2013. Harvesting ambient geospatial information from social media feeds. GeoJournal 78, 2: 319--338. http://doi.org/10.1007/s10708-011--9438--2

[44]

Andrea H. Tapia, Nicolas Lalone, Elizabeth MacDonald, Michelle Hall, Nathan Case, and Matt Heavner. 2014. AURORASAURUS: Citizen Science, Early Warning Systems and Space Weather. HCOMP.

[45]

Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, and Hiroyuki Ohsaki. 2015. Recognizing Depression from Twitter Activity. CHI, ACM Press, 3187--3196. http://doi.org/10.1145/2702123.2702280

Digital Library

[46]

Andranik Tumasjan, Timm Oliver Sprenger, Philipp G. Sandner, and Isabell M. Welpe. 2010. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM 10: 178--185.

[47]

Alessandro Venerandi, Daniele Quercia, and Diego Saez-trumper. 2015. Measuring Urban Deprivation from User Generated Content. CSCW.

Digital Library

[48]

Ting-Yu Wang, F. Maxwell Harper, and Brent Hecht. 2014. Designing Better Location Fields in User Profiles. Proceedings of the 18th International Conference on Supporting Group Work, ACM, 73--80. http://doi.org/10.1145/2660398.2660424

Digital Library

[49]

Spencer a Wood, Anne D Guerry, Jessica M Silver, and Martin Lacayo. 2013. Using social media to quantify nature-based tourism and recreation. Scientific reports 3: 2976. http://doi.org/10.1038/srep02976

[50]

Amy X. Zhang and Scott Counts. 2015. Modeling Ideology and Predicting Policy Change with Social Media: Case of Same-Sex Marriage. CHI, ACM Press, 2603--2612. http://doi.org/10.1145/2702123.2702193

Digital Library

[51]

Yan-Tao Zheng, Zheng-Jun Zha, and Tat-Seng Chua. 2012. Mining Travel Patterns from Geotagged Photos. ACM TIST 3, 3: 1--18. http://doi.org/10.1145/2168752.2168770

Digital Library

[52]

Dennis Zielstra, Hartwig Hochmair, Pascal Neis, and Francesco Tonini. 2014. Areal Delineation of Home Regions from Contribution and Editing Patterns in OpenStreetMap. ISPRS International Journal of GeoInformation 3, 4: 1211--1233. http://doi.org/10.3390/ijgi3041211

Cited By

Perreault BLee JShava RMustafaraj E(2024)Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information EnvironmentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658916(433-443)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658916
Abouk RJalali NPapatla P(2024)Can tweets be word of mouth that changes risky behaviors?Journal of Business Research10.1016/j.jbusres.2024.114501174(114501)Online publication date: Mar-2024
https://doi.org/10.1016/j.jbusres.2024.114501
Wang JKim GChang K(2023)Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learningInternational Journal of Health Geographics10.1186/s12942-023-00344-522:1Online publication date: 16-Sep-2023
https://doi.org/10.1186/s12942-023-00344-5
Show More Cited By

Index Terms

The Geography and Importance of Localness in Geotagged Social Media
1. Human-centered computing

Recommendations

Barriers to the Localness of Volunteered Geographic Information
CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Localness is an oft-cited benefit of volunteered geographic information (VGI). This study examines whether localness is a constant, universally shared benefit of VGI, or one that varies depending on the context in which it is produced. Focusing on ...
Platial or Locational Data? Toward the Characterization of Social Location Sharing
HICSS '15: Proceedings of the 2015 48th Hawaii International Conference on System Sciences

Sharing "location" information on social media became commonplace since the advent of smartphones. Location-based social networks introduced a derivative form of Volunteered Geographic Information (VGI) known as Social Location Sharing (SLS). It ...
Social Media Analytics and Intelligence

In a broad sense, social media refers to a conversational, distributed mode of content generation, dissemination, and communication among communities. Different from broadcast-based traditional and industrial media, social media has torn down the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 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 ACM 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]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

CHI'16

Sponsor:

SIGCHI

CHI'16: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2016

California, San Jose, USA

Acceptance Rates

CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
1,507
Total Downloads

Downloads (Last 12 months)164
Downloads (Last 6 weeks)19

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Perreault BLee JShava RMustafaraj E(2024)Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information EnvironmentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658916(433-443)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658916
Abouk RJalali NPapatla P(2024)Can tweets be word of mouth that changes risky behaviors?Journal of Business Research10.1016/j.jbusres.2024.114501174(114501)Online publication date: Mar-2024
https://doi.org/10.1016/j.jbusres.2024.114501
Wang JKim GChang K(2023)Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learningInternational Journal of Health Geographics10.1186/s12942-023-00344-522:1Online publication date: 16-Sep-2023
https://doi.org/10.1186/s12942-023-00344-5
Naggita KLaChance JXiang A(2023)Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual DataProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604659(520-530)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604659
Rimjhim Dandapat S(2023)Tagging Multi-Label Categories to Points of Interest From Check-In DataIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32293387:4(1191-1204)Online publication date: Aug-2023
https://doi.org/10.1109/TETCI.2022.3229338
Owuor IHochmair H(2023)Analysing the effect of COVID-19 on the localness of visitors to Florida state parks and New York attractions using online reviews, tweets, and SafeGraph travel patternsJournal of Location Based Services10.1080/17489725.2023.229236318:1(118-138)Online publication date: 8-Dec-2023
https://doi.org/10.1080/17489725.2023.2292363
Mast JSapena MMühlbauer MBiewer CTaubenböck H(2023)The migrant perspective: Measuring migrants' movements and interests using geolocated tweetsPopulation, Space and Place10.1002/psp.2732Online publication date: 27-Nov-2023
https://doi.org/10.1002/psp.2732
Cao JHochmair HBasheeh F(2022)The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter UsersGeographies10.3390/geographies20300332:3(549-562)Online publication date: 5-Sep-2022
https://doi.org/10.3390/geographies2030033
Ostermann F(2021)Linking Geosocial Sensing with the Socio-Demographic Fabric of Smart CitiesISPRS International Journal of Geo-Information10.3390/ijgi1002005210:2(52)Online publication date: 27-Jan-2021
https://doi.org/10.3390/ijgi10020052
Westerholt R(2021)Emphasising spatial structure in geosocial media data using spatial amplifier filteringEnvironment and Planning B: Urban Analytics and City Science10.1177/239980832098723548:9(2842-2861)Online publication date: 8-Feb-2021
https://doi.org/10.1177/2399808320987235
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents