Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship

Won Yong Shin; Jaehee Cho; André M. Everett

doi:10.1145/2820783.2820841

Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship

Won Yong Shin, Jaehee Cho, André M. Everett

Department of Computational Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
Editors	Yan Huang, Mohamed Ali, Jagan Sankaranarayanan, Matthias Renz, Michael Gertz
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450339674
DOIs	https://doi.org/10.1145/2820783.2820841
Publication status	Published - 2015 Nov 3
Event	23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 - Seattle, United States Duration: 2015 Nov 3 → 2015 Nov 6

Publication series

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume	03-06-November-2015

Conference

Conference	23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
Country/Territory	United States
City	Seattle
Period	15/11/3 → 15/11/6

Bibliographical note

Publisher Copyright:
© 2015 ACM.

All Science Journal Classification (ASJC) codes

Earth-Surface Processes
Computer Science Applications
Modelling and Simulation
Computer Graphics and Computer-Aided Design
Information Systems

Access to Document

10.1145/2820783.2820841

Cite this

Shin, W. Y., Cho, J., & Everett, A. M. (2015). Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship. In Y. Huang, M. Ali, J. Sankaranarayanan, M. Renz, & M. Gertz (Eds.), 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 [a51] (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems; Vol. 03-06-November-2015). Association for Computing Machinery. https://doi.org/10.1145/2820783.2820841

Shin, Won Yong ; Cho, Jaehee ; Everett, André M. / Clarifying the role of distance in friendships on Twitter : Discovery of a double power-law relationship. 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015. editor / Yan Huang ; Mohamed Ali ; Jagan Sankaranarayanan ; Matthias Renz ; Michael Gertz. Association for Computing Machinery, 2015. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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title = "Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship",

abstract = "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.",

author = "Shin, {Won Yong} and Jaehee Cho and Everett, {Andr{\'e} M.}",

note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 ; Conference date: 03-11-2015 Through 06-11-2015",

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doi = "10.1145/2820783.2820841",

language = "English",

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Shin, WY, Cho, J & Everett, AM 2015, Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship. in Y Huang, M Ali, J Sankaranarayanan, M Renz & M Gertz (eds), 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015., a51, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, vol. 03-06-November-2015, Association for Computing Machinery, 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015, Seattle, United States, 15/11/3. https://doi.org/10.1145/2820783.2820841

Clarifying the role of distance in friendships on Twitter : Discovery of a double power-law relationship. / Shin, Won Yong; Cho, Jaehee; Everett, André M.

23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015. ed. / Yan Huang; Mohamed Ali; Jagan Sankaranarayanan; Matthias Renz; Michael Gertz. Association for Computing Machinery, 2015. a51 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems; Vol. 03-06-November-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Everett, André M.

PY - 2015/11/3

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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.

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Shin WY, Cho J, Everett AM. Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship. In Huang Y, Ali M, Sankaranarayanan J, Renz M, Gertz M, editors, 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015. Association for Computing Machinery. 2015. a51. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). https://doi.org/10.1145/2820783.2820841

Clarifying the role of distance in friendships on Twitter: Discovery of a double power-law relationship

Abstract

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