TY - GEN
T1 - Clarifying the role of distance in friendships on Twitter
T2 - 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
AU - Shin, Won Yong
AU - Cho, Jaehee
AU - Everett, André M.
N1 - Publisher Copyright:
© 2015 ACM.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/11/3
Y1 - 2015/11/3
N2 - This study analyzes friendships in online social networks involving geographic distance with a geo-referenced Twitter dataset, which provides the exact distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter, requiring bidirectional communication. Next, by utilizing geo-tagged mentions delivered by users to determine their locations, we introduce a two-stage distance estimation algorithm. As our main contribution, our study provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides much more fine-grained social ties in space, compared to the conventional results showing a homogeneous power-law with distance.
AB - This study analyzes friendships in online social networks involving geographic distance with a geo-referenced Twitter dataset, which provides the exact distance between corresponding users. We start by introducing a strong definition of "friend" on Twitter, requiring bidirectional communication. Next, by utilizing geo-tagged mentions delivered by users to determine their locations, we introduce a two-stage distance estimation algorithm. As our main contribution, our study provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides much more fine-grained social ties in space, compared to the conventional results showing a homogeneous power-law with distance.
UR - http://www.scopus.com/inward/record.url?scp=84961216003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961216003&partnerID=8YFLogxK
U2 - 10.1145/2820783.2820841
DO - 10.1145/2820783.2820841
M3 - Conference contribution
AN - SCOPUS:84961216003
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
A2 - Huang, Yan
A2 - Ali, Mohamed
A2 - Sankaranarayanan, Jagan
A2 - Renz, Matthias
A2 - Gertz, Michael
PB - Association for Computing Machinery
Y2 - 3 November 2015 through 6 November 2015
ER -