Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/20227
Title: | The Geography and Importance of Localness in Geotagged Social Media | Authors: | Johnson, Isaac Sengupta, Subhasree SCHOENING, Johannes Hecht, Brent |
Issue Date: | 2016 | Publisher: | ACM | Source: | Proceedings of the International Conference on Human Factors in Computing Systems, (2016) | 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 VGIbased 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. | Notes: | Johnson, IL (reprint author), Univ Minho, Dept Comp Sci, GroupLens Res, P-4719 Braga, Portugal. ijohnson@cs.umn.edu; sengu025@umn.edu; johannes.schoening@uhasselt.be; bhecht@cs.umn.edu | Keywords: | geotagged social media; volunteered geographic information; localness; user-generated content | Document URI: | http://hdl.handle.net/1942/20227 | ISBN: | 978-1-4503-3362-7 | DOI: | 10.1145/2858036.2858122 | ISI #: | 000380532900047 | Rights: | 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 Permissions@acm.org. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
paper554.pdf | Peer-reviewed author version | 770.03 kB | Adobe PDF | View/Open |
2858036.2858122.pdf Restricted Access | Published version | 1.17 MB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
28
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
37
checked on Oct 14, 2024
Page view(s)
80
checked on Sep 5, 2022
Download(s)
124
checked on Sep 5, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.