The Australian Twittersphere in 2016: Mapping the Follower/Followee Network
Open accessAbstract
Twitter is now a key platform for public communication between a diverse range of participants, but the overall shape of the communication network it provides remains largely unknown. This article provides a detailed overview of the network structure of the Australian Twittersphere and identifies the thematic drivers of the key clusters within the network. We identify some 3.72 million Australian Twitter accounts and map the follower/followee connections between the 255,000 most connected accounts; we utilize community detection algorithms to identify the major clusters within this network and examine their account populations to identify their constitutive themes; we examine account creation dates and reconstruct a timeline for the Twitter adoption process among different communities; and we examine lifetime and recent tweeting patterns to determine the historically and currently most active clusters in the network. In combination, this offers the first rigorous and comprehensive study of the network structure of an entire national Twittersphere.
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Twitter follower maps in Australia and Norway show interest-driven clusters of dense interconnection but few significant disconnects across the overall network.
“For Twitter, comprehensive maps of follower connections in the Australian (Bruns et al., 2017) and Norwegian Twitterspheres (Bruns & Enli, 2018) have similarly shown the existence of interest-driven clusters of dense interconnection around topics from politics to sports, but point to few significant disconnects across the overall network.”
From Filter bubble (2019) · cites this paper as Bruns et al., 2017
The Analysis and Network Structure sections describe densely connected, predominantly thematic clusters in the Australian follower network, including politics and sports. The paper also describes some clusters as comparatively isolated, but does not establish that there are few significant disconnects across the overall network; its principal map focuses on 255,000 best-connected accounts. The Norwegian findings belong to the separately cited source and are not assessed here. (LLM, conf=0.82)
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Self-citation
- Authors
- Bruns, A., Moon, B., Münch, F., & Sadkowsky, T.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- Bruns, Axel
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2017-10
- Volume
- 3
- Issue
- 4
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W2771918561
- Cited by
- 81
- References
- 23
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 18 · 2022: 15 · 2021: 14 · 2020: 3 · 2019: 12 · 2018: 5
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Social Media + Society (publishedVersion, cc-by-nc)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Keywords
- Twitter, network structure, community detection, network clusters, social media adoption, public communication
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