Skip to main content
Top
Published in: Social Network Analysis and Mining 1/2020

01-12-2020 | Original Article

Examining the evolution of the Twitter elite network

Authors: Reza Motamedi, Soheil Jamshidi, Reza Rejaie, Walter Willinger

Published in: Social Network Analysis and Mining | Issue 1/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The most-followed Twitter users and their pairwise relationships form a subgraph of Twitter users that we call the Twitter elite network. The connectivity patterns and information exchanges (in terms of replies and retweets) among these elite users illustrate how the “important” users connect and interact with one another on Twitter. At the same time, such an elite-focused view also provides valuable information about the structure of the Twitter network as a whole. This paper presents a detailed characterization of the structure and evolution of the top 10K Twitter elite network. We describe our technique for efficiently and accurately constructing the Twitter elite network along with social attributes of individual elite accounts and apply it to capture two snapshots of the top 10K elite network that are some 2.75 years apart. We show that a sufficiently large elite network is typically composed of 14–20 stable and cohesive communities that are recognizable in both snapshots, thus representing “socially meaningful” components of the elite network. We examine the changes in the identity and connectivity of individual elite users over time and characterize the community-level structure of the elite network in terms of bias in directed pairwise connectivity and relative reachability. We also show that both the reply and retweet activity between elite users are effectively contained within individual elite communities and are generally aligned with the centrality of the elite community users in both snapshots of the elite network. Finally, we observe that the majority of the regular Twitter users tend to have elite friends that belong to a single elite community. This finding offers a promising criterion for grouping regular users into “shadow partitions” based on their association with elite communities.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Footnotes
1
In Twitter, each user u has a collection of followers that receive any tweets that u sends. u is called a friend for each one of its followers.
 
2
We use the terms nodes with the highest degree and the most-followed accounts interchangeably.
 
3
A user with many followers that is part of a partition or weakly connected region is not likely to be discovered by random walks. We argue that such an elite user is less important for our analysis.
 
4
The average degree obtained by dividing the number of directed edged |E| by the number of nodes given by the size of the elite network or view in the first column increases from roughly 40 to 115 for S16 and 37 to 110 for S18.
 
5
In a randomized degree-preserving version of the network, we randomly connect elite nodes while maintaining their in- and out-degrees.
 
6
We use a simple reordering algorithm along the x-axis to group unstable nodes that have a similar co-appearance pattern. Note that the sum of the values in each column is not \(100\%\) since a co-appearance of an unstable node with multiple resilient communities is counted separately.
 
Literature
go back to reference Al-Garadi M (2018) Analysis of online social network connections for identification of influential users: survey and open research issues. ACM Comput Surv 51:1–34CrossRef Al-Garadi M (2018) Analysis of online social network connections for identification of influential users: survey and open research issues. ACM Comput Surv 51:1–34CrossRef
go back to reference Avrachenkov K, Litvak N, Prokhorenkova LO, Suyargulova E (2014) Quick detection of high-degree entities in large directed networks. In: Proceedings of ICDM, IEEE Avrachenkov K, Litvak N, Prokhorenkova LO, Suyargulova E (2014) Quick detection of high-degree entities in large directed networks. In: Proceedings of ICDM, IEEE
go back to reference Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of WSDM, ACM Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of WSDM, ACM
go back to reference Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:2008MATH Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:2008MATH
go back to reference Bollobás B (2013) Modern graph theory, vol 184. Springer, New YorkMATH Bollobás B (2013) Modern graph theory, vol 184. Springer, New YorkMATH
go back to reference Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proceedings of WWW, ACM Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proceedings of WWW, ACM
go back to reference Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in Twitter: the million follower fallacy. In: ICWSM Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in Twitter: the million follower fallacy. In: ICWSM
go back to reference Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the Flickr social network. In: Proceedings of WWW, ACM Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the Flickr social network. In: Proceedings of WWW, ACM
go back to reference Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, CambridgeCrossRef Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, CambridgeCrossRef
go back to reference Gonzalez R, Cuevas R, Motamedi R, Rejaie R, Cuevas A (2016) Assessing the evolution of google+ in its first two years. IEEE/ACM Trans Netw (ToN) 24(3):1813–1826CrossRef Gonzalez R, Cuevas R, Motamedi R, Rejaie R, Cuevas A (2016) Assessing the evolution of google+ in its first two years. IEEE/ACM Trans Netw (ToN) 24(3):1813–1826CrossRef
go back to reference Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of WWW, ACM Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of WWW, ACM
go back to reference Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123MathSciNetCrossRef Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123MathSciNetCrossRef
go back to reference Motamedi R, Rejaie R, Lowd D, Willinger W, Gonzalez R (2014) Inferring coarse views of connectivity in very large graphs. In: Proceedings of COSN Motamedi R, Rejaie R, Lowd D, Willinger W, Gonzalez R (2014) Inferring coarse views of connectivity in very large graphs. In: Proceedings of COSN
go back to reference Motamedi R, Rezayi S, Rejaie R, Willinger W (2018) On characterizing the twitter elite network. In: 2018 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM), pp 234–241 Motamedi R, Rezayi S, Rejaie R, Willinger W (2018) On characterizing the twitter elite network. In: 2018 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM), pp 234–241
go back to reference Puranik T, Narayanan L (2017) Community detection in evolving networks. In: Proceedings of ASONAM Puranik T, Narayanan L (2017) Community detection in evolving networks. In: Proceedings of ASONAM
go back to reference Rejaie R, Torkjazi M, Valafar M, Willinger W (2010) Sizing up online social networks. IEEE Netw 24(5):32–37CrossRef Rejaie R, Torkjazi M, Valafar M, Willinger W (2010) Sizing up online social networks. IEEE Netw 24(5):32–37CrossRef
go back to reference Rosvall M, Bergstrom C (2007) Maps of information flow reveal community structure in complex networks. In: Proceedings of the national academy of sciences Rosvall M, Bergstrom C (2007) Maps of information flow reveal community structure in complex networks. In: Proceedings of the national academy of sciences
go back to reference Sobolevsky S, Campari R, Belyi A, Ratti C (2014) General optimization technique for high-quality community detection in complex networks. Phys Rev E 90(1):012811-1–012811-8CrossRef Sobolevsky S, Campari R, Belyi A, Ratti C (2014) General optimization technique for high-quality community detection in complex networks. Phys Rev E 90(1):012811-1–012811-8CrossRef
go back to reference Stutzbach D, Rejaie R, Duffield N, Sen S, Willinger W (2009) On unbiased sampling for unstructured peer-to-peer networks. IEEE/ACM Trans Netw 17(2):377–390CrossRef Stutzbach D, Rejaie R, Duffield N, Sen S, Willinger W (2009) On unbiased sampling for unstructured peer-to-peer networks. IEEE/ACM Trans Netw 17(2):377–390CrossRef
go back to reference Torkjazi M, Rejaie R, Willinger W (2009) Hot today, gone tomorrow: on the migration of myspace users. In: Proceedings of the ACM workshop on online social networks, pp 43–48 Torkjazi M, Rejaie R, Willinger W (2009) Hot today, gone tomorrow: on the migration of myspace users. In: Proceedings of the ACM workshop on online social networks, pp 43–48
go back to reference Valafar M, Rejaie R, Willinger W (2009) Beyond friendship graphs: a study of user interactions in flickr. In: Proceedings of the ACM workshop on online social networks, pp 25–30 Valafar M, Rejaie R, Willinger W (2009) Beyond friendship graphs: a study of user interactions in flickr. In: Proceedings of the ACM workshop on online social networks, pp 25–30
go back to reference Yang J, Leskovec J (2013) Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of WSDM Yang J, Leskovec J (2013) Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of WSDM
Metadata
Title
Examining the evolution of the Twitter elite network
Authors
Reza Motamedi
Soheil Jamshidi
Reza Rejaie
Walter Willinger
Publication date
01-12-2020
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2020
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-019-0612-8

Other articles of this Issue 1/2020

Social Network Analysis and Mining 1/2020 Go to the issue

Premium Partner