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Examining the evolution of the Twitter elite network

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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.

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Notes

  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.

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Acknowledgements

We would like to thank Hooman Mostafavi for his help in collecting the second snapshot of the Twitter elite network. This material is based upon work supported by the National Science Foundation under Grant IIS-0917381 and CNS-1320977.

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Authors and Affiliations

  1. University of Oregon, Eugene, USA

    Reza Motamedi, Soheil Jamshidi & Reza Rejaie

  2. NIKSUN, Inc., Princeton, USA

    Walter Willinger

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  1. Reza Motamedi
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  2. Soheil Jamshidi
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  3. Reza Rejaie
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  4. Walter Willinger
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Correspondence to Reza Rejaie.

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Motamedi, R., Jamshidi, S., Rejaie, R. et al. Examining the evolution of the Twitter elite network. Soc. Netw. Anal. Min. 10, 1 (2020). https://doi.org/10.1007/s13278-019-0612-8

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