The Norwegian Twittersphere: Structure and Dynamics
Open accessAbstract
Abstract This article takes a new approach to the comprehensive study of an entire national Twitter-sphere. It identifies, to the extent that this is possible with the data made available through Twitter’s Application Programming Interface (API), all accounts operated by Norwegian users and institutions, analyses patterns in their public profile information, and maps their follower/followee connections with each other. This provides new insights into the historical development of the Norwegian Twittersphere, its current network structure and the presence of diverse interests and issues amongst the nearly one million accounts within this community. Its findings also constitute important background information for future Twitter research that takes the familiar hashtag studies route: its observations enable such studies to filter their datasets for confirmed Norwegian accounts only, and to examine the presence of accounts with specific interest profiles, as determined by the present study, in their datasets.
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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 & Enli, 2018
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FILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849 · see citing works
The Australian Twittersphere in 2016: Mapping the Follower/Followee Network
Bruns, A., Moon, B., Münch, F., & Sadkowsky, T. (2017) · Social Media + Society · doi:10.1177/2056305117748162 · Cited by 81 · see citing works
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Research works printed but not linked: 26
- Bastian, Mathieu; Heymann, Sebastien & Jacomy, Mathieu (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks, pp. 361-362 in Proceedings of the Third International ICWSM Conference. San José: AAAI Press. Available at <http://www.aaai.org/ocs/index.php/ICWSM/09/paper/ viewFile/154/1009/> [Accessed 12 June 2017].
- Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud & Lefebvre, Etienne (2008). Fast Unfold- ing of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment, 10: P10008. doi:10.1088/1742-5468/2008/10/P10008. · DOI · Paper page
- Bruns, Axel & Burgess, Jean (2015). Twitter Hashtags from Ad Hoc to Calculated Publics, pp. 13-28 in Rambukkana, Nathan (ed.) Hashtag Publics: The Power and Politics of Discursive Networks. New York: Peter Lang.
- Bruns, Axel; Burgess, Jean & Highfield, Tim (2014). A "Big Data" Approach to Mapping the Australian Twit- tersphere, pp. 113-29 in Arthur, Paul Longley & Bode, Katherine (eds.) Advancing Digital Humanities: Research, Methods, Theories. Houndmills, UK: Palgrave Macmillan.
- Burgess, Jean & Bruns, Axel (2015). Easy Data, Hard Data: The Politics and Pragmatics of Twitter Research after the Computational Turn, pp. 93-111 in Langlois, Ganaele; Redden, Joanna & Elmer, Greg (eds.) Compromised Data: From Social Media to Big Data. New York: Bloomsbury Academic.
- Burgess, Jean & Crawford, Kate (2011). Acute Events in Social Media. Paper presented at the Association of Internet Researchers conference, 10 October 2011, Seattle. Available at <https://www.slideshare.net/ jeangenie/acute-events-in-social-media> [Accessed 12 June 2017].
- Colleoni, Eleanor; Rozza, Alessandro & Arvidsson, Adam (2014). Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data. Journal of Com- munication, 64(2): 317-332. doi:10.1111/jcom.12084. · DOI · Paper page
- Conover, Michael D.; Ratkiewicz, Jacob; Francisco, Matthew; Goncalves, Bruno; Menczer, Filippo & Flam- mini, Alessandro (2011). Political Polarization on Twitter. Paper presented at the Fifth International AAAI Conference on Weblogs and Social Media. Available at <http://www.aaai.org/ocs/index.php/ICWSM/ ICWSM11/paper/view/2847> [Accessed 12 June 2017].
- Crawford, Kate (2009). Following You: Disciplines of Listening in Social Media. Continuum, 23(4): 525-35. doi:10.1080/10304310903003270. · DOI
- Hermida, Alfred (2014). Twitter as an Ambient News Network, pp. 359-72 in Weller, Katrin; Bruns, Axel; Burgess, Jean; Mahrt, Merja & Puschmann, Cornelius (eds.) Twitter and Society. New York: Peter Lang.
- Highfield, Tim (2013). Following the Yellow Jersey: Tweeting the Tour de France, pp. 249-261 in Weller, Katrin; Bruns, Axel; Burgess, Jean; Mahrt, Merja & Puschmann, Cornelius (eds.) Twitter and Society. New York: Peter Lang.
- Jacomy, Mathieu; Venturini, Tommaso; Heymann, Sebastien & Bastian, Mathieu (2014). ForceAtlas2, a Con- tinuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLOS ONE, 9(6): e98679. doi:10.1371/journal.pone.0098679. · DOI · Paper page
- Kaufmann, Mareile (2015). Resilience 2.0: Social Media Use and (Self-) Care during the 2011 Norway At- tacks. Media, Culture & Society, 37(7): 972-987.
- Krackhardt, David & Stern, Robert N. (1988). Informal Networks and Organizational Crises: An Experimental Simulation. Social Psychology Quarterly, 51(2): 123-40. doi:10.2307/2786835. · DOI · Paper page
- Larsson, Anders Olof & Moe, Hallvard (2013). Twitter in Politics and Elections: Insights from Scandinavia, pp. 319-330 in Weller, Katrin; Bruns, Axel; Burgess, Jean; Mahrt, Merja & Puschmann, Cornelius (eds.), Twitter and Society. New York: Peter Lang.
- Meraz, Sharon & Papacharissi, Zizi (2013). Networked Gatekeeping and Networked Framing on #Egypt. The International Journal of Press/Politics, 18(2): 138-66. doi:10.1177/1940161212474472. · DOI
- Papacharissi, Zizi (2014). Affective Publics: Sentiment, Technology, and Politics. Oxford University Press.
- Papacharissi, Zizi & de Fatima Oliveira, Maria (2012). Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication, 62: 266-82. doi:10.1111/j.1460- 2466.2012.01630.x. · DOI · Paper page
- Pariser, Eli (2012). The Filter Bubble: What the Internet Is Hiding from You. London: Penguin.
- Perng, Sung-Yueh; Buscher, Monika; Wood, Lisa; Halvorsrud, Ragnhild; Stiso, Michael; Ramirez, Leonardo & Al-Akkad, Amro (2013). Peripheral Response: Microblogging during the 22/7/2011 Norway At- tacks. International Journal of Information Systems for Crisis Response and Management, 5(1): 41-57.
- Rambukkana, Nathan (ed.) (2015). Hashtag Publics: The Power and Politics of Discursive Networks. New York: Peter Lang.
- Skogerbø, Eli & Moe, Hallvard (2015). Twitter på tvers -koblinger mellom journalister og politikere [Across Twitter -Connections between Journalists and Politicians]. Norsk Medietidsskrift, 22(3): 1-21.
- Sunstein, Cass R. (2009). Republic.com 2.0. Princeton, NJ: Princeton University Press.
- Sunstein, Cass R. (2017). #Republic: Divided Democracy in the Age of Social Media. Princeton, NJ: Princeton University Press.
- Syvertsen, Trine; Enli, Gunn; Mjøs, Ole J. & Moe, Hallvard (2014). The Media Welfare State: Nordic Media in the Digital Era. Ann Arbor: University of Michigan Press.
- Vis, Farida (2013). Twitter as a Reporting Tool for Breaking News: Journalists Tweeting the 2011 UK Riots. Digital Journalism, 1(1): 27-47. doi:10.1080/21670811.2012.741316. · DOI
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Bruns, A. (2019) · Internet Policy Review · doi:10.14763/2019.4.1426 · Cited by 193 · see citing works
“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…”
BackgroundSelf-citation
- Authors
- Bruns, A., & Enli, G.
- Affiliations
- Queensland University of Technology (AU)
- University of Oslo (NO)
- Venue
- Nordicom Review
- Publisher
- Walter de Gruyter GmbH
- Published
- 2018-05-01
- Volume
- 39
- Issue
- 1
- Pages
- 129-148
- ISSN
- 1403-1108 (ISSN-L)
- 2001-5119
- OpenAlex
- W2899537647
- Cited by
- 45
- References
- 2
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 17 · 2022: 10 · 2021: 6 · 2020: 1 · 2019: 5
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Nordicom review/NORDICOM review (publishedVersion, cc-by-nc-nd)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
- Funders
- Norges Forskningsråd
- Australian Research Council
- Topics
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- Twitter, social media, network structure, user profile data
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