TY - GEN
T1 - The geography and importance of localness in geotagged social media
AU - Johnson, Isaac L.
AU - Sengupta, Subhasree
AU - Schöning, Johannes
AU - Hecht, Brent
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/5/7
Y1 - 2016/5/7
N2 - 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.
AB - 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.
KW - Geotagged social media
KW - Localness
KW - User-generated content
KW - Volunteered geographic information
UR - http://www.scopus.com/inward/record.url?scp=85015075781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015075781&partnerID=8YFLogxK
U2 - 10.1145/2858036.2858122
DO - 10.1145/2858036.2858122
M3 - Conference contribution
AN - SCOPUS:85015075781
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 515
EP - 526
BT - CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 34th Annual Conference on Human Factors in Computing Systems, CHI 2016
Y2 - 7 May 2016 through 12 May 2016
ER -