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Assessing Twitter Geocoding Resolution

Published: 15 May 2018 Publication History

Abstract

User-defined location privacy settings on Twitter cause geolocated tweets to be placed at four different resolutions: precise, point of interest (POI), neighbourhood and city levels. The latter two levels are not described by Twitter or the API, resulting in a risk that clustered tweets are unintentionally treated as real clusters in spatial analyses. This paper outlines a framework to address these differing spatial resolutions and highlight the impact they can have on cartographic representations. As part of this framework this paper also outlines a method of discovering sources (third-party applications) that produce geolocated tweets but do not reflect genuine human activity. We found that including tweets at all spatial resolutions created an artificially inflated importance of certain locations within a city. Discovering device-level geocoded tweets was straight forward, but querying Foursquare's API was required to differentiate between neighbourhood level clusters and POIs.

References

[1]
Philip Adams and Antonio Remiro-Azócar. 2016. City-wide Mobility Mapping Using Social Media Communications. Ph.D. Dissertation. Unversity of Bath.
[2]
Junghoon Chae, Dennis Thom, Harald Bosch, Yun Jang, Ross Maciejewski, David S. Ebert, and Thomas Ertl. 2012. Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In IEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings. IEEE, 143--152.
[3]
Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2010. Who is Tweeting on Twitter: Human, Bot, or Cyborg? Acsac 2010 (2010), 21.
[4]
Eric M. Clark, Jake Ryland Williams, Chris A. Jones, Richard A. Galbraith, Christopher M. Danforth, and Peter Sheridan Dodds. 2016. Sifting robotic from organic text: A natural language approach for detecting automation on Twitter. Journal of Computational Science 16 (2016), 1--7. arXiv:1505.04342
[5]
Michael Clemence, Carl Miller, Steve Ginnis, Rowena Stobart, and Alex Krasodomski-Jones. 2015. The Road to Representivity. Technical Report. Ipsos MORI, London. http://www.demos.co.uk/wp-content/uploads/2015/09/Road
[6]
Xiaowen Dong, Dimitrios Mavroeidis, Francesco Calabrese, and Pascal Frossard. 2015. Multiscale event detection in social media. Data Mining and Knowledge Discovery 29, 5 (2015), 1374--1405. arXiv:arXiv:1404.7048v1
[7]
Zafar Gilani, Reza Farahbakhsh, Gareth Tyson, Liang Wang, and Jon Crowcroft. 2017. An in-depth characterisation of Bots and Humans on Twitter. arXiv preprint arXiv:1704.01508 (2017). arXiv:1704.01508 https://arxiv.org/pdf/1704. 01508.pdfhttp://arxiv.org/abs/1704.01508
[8]
Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. 2017. Classification of Twitter Accounts into Automated Agents and Human Users. In Proceedings of ASONAM 2017, the 2017 International Conference on Advances in Social Networks Analysis and Mining.
[9]
Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, and Patrick Meier. 2013. Extracting information nuggets from disaster-related messages in social media. ISCRAM, Baden- . . . May (nov 2013), 791--800.
[10]
Alyson Lloyd and James Cheshire. 2017. Deriving retail centre locations and catchments from geo-tagged Twitter data. Computers, Environment and Urban Systems 61 (2017), 108--118.
[11]
Diana Maynard, Kalina Bontcheva, and Dominic Rout. 2012. Challenges in developing opinion mining tools for social media. In LREC 2012. 15--22. http://www.lrec-conf.org/proceedings/lrec2012/workshops/21. LREC2012NLP4UGCProceedings.pdf
[12]
Alan Mislove, Sune Lehmann, Yong-yeol Ahn, Jukka-pekka Onnela, and J Niels Rosenquist. 2011. Understanding the Demographics of Twitter Users. Artificial Intelligence (2011), 554--557. http://www.aaai.org/ocs/index.php/ICWSM/ ICWSM11/paper/viewFile/2816/3234
[13]
Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. WWW '10: Proceedings of the 19th international conference on World wide web (2010), 851. arXiv:0808.0743v3
[14]
L. Smith, Q. Liang, P. James, and W. Lin. 2015. Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework. Journal of Flood Risk Management (apr 2015).
[15]
Enrico Steiger, René Westerholt, Bernd Resch, and Alexander Zipf. 2015. Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. Computers, Environment and Urban Systems 54 (2015), 255--265.
[16]
Avaré Stewart, Sara Romano, Nattiya Kanhabua, Sergio Di Martino, Wolf Siberski, Antonino Mazzeo, Wolfgang Nejdl, and Ernesto Diaz-Aviles. 2016. Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter? arXiv preprint arXiv:1611.03426 (2016). arXiv:1611.03426 https://arxiv.org/pdf/1611. 03426.pdfhttp://arxiv.org/abs/1611.03426
[17]
Sayan Unankard, Xue Li, and Mohamed A. Sharaf. 2015. Emerging event detection in social networks with location sensitivity. World Wide Web 18, 5 (2015), 1393-- 1417.
[18]
Liang Zhao, Junxiang Wang, Feng Chen, Chang Tien Lu, and Naren Ramakrishnan. 2017. Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization. Proc. IEEE 105, 10 (2017), 1953--1970.

Cited By

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  • (2023)Veri Madenciliği Uygulamalarının Web Tabanlı Mekânsal Görsel Analitik Ortamda Sunumu: COVID-19 Aşı Tweet’leri ÖrneğiPresentation of Data Mining Applications in Web Based Geovisual Analytical Environment: Example of COVID-19 Vaccine TweetsAfyon Kocatepe University Journal of Sciences and Engineering10.35414/akufemubid.120685123:2(417-426)Online publication date: 3-May-2023
  • (2022)Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning modelScientific Reports10.1038/s41598-022-05974-612:1Online publication date: 3-Feb-2022
  • (2020)Design and analysis of a large-scale COVID-19 tweets datasetApplied Intelligence10.1007/s10489-020-02029-zOnline publication date: 6-Nov-2020

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cover image ACM Conferences
WebSci '18: Proceedings of the 10th ACM Conference on Web Science
May 2018
399 pages
ISBN:9781450355636
DOI:10.1145/3201064
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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New York, NY, United States

Publication History

Published: 15 May 2018

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Author Tags

  1. census
  2. framework
  3. mapping
  4. poi
  5. twitter

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  • Short-paper

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  • Ordnance Survey

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WebSci '18
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WebSci '18: 10th ACM Conference on Web Science
May 27 - 30, 2018
Amsterdam, Netherlands

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WebSci '18 Paper Acceptance Rate 30 of 113 submissions, 27%;
Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

View all
  • (2023)Veri Madenciliği Uygulamalarının Web Tabanlı Mekânsal Görsel Analitik Ortamda Sunumu: COVID-19 Aşı Tweet’leri ÖrneğiPresentation of Data Mining Applications in Web Based Geovisual Analytical Environment: Example of COVID-19 Vaccine TweetsAfyon Kocatepe University Journal of Sciences and Engineering10.35414/akufemubid.120685123:2(417-426)Online publication date: 3-May-2023
  • (2022)Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning modelScientific Reports10.1038/s41598-022-05974-612:1Online publication date: 3-Feb-2022
  • (2020)Design and analysis of a large-scale COVID-19 tweets datasetApplied Intelligence10.1007/s10489-020-02029-zOnline publication date: 6-Nov-2020

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