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Introduction
The journal Network Science (Cambridge University Press) invites submissions to a Special Issue on network polarisation.
As the essence of the liberal model of democracy embraces competing agendas, substantial debate, and diversity, it becomes a hazard when citizens start to harbour “fear and loathing” toward the “other side” (Iyengar & Westwood, 2015). Polarisation can undermine the foundation of healthy debate, compromise political dialogue quality, and increase hostility and incommunicability between differing groups.
The homophily tendency to create ties with like-minded individuals (McPherson et al.,2001), possibly reinforced by algorithmic personalization in the online realm (Pariser, 2011), has been linked to the fragmentation of society into clusters of individuals selectively exposed to partisan information, reinforcing biases and polarisation (Sunstein, 2001). Ideologically polarised network structures have been observed in the blogosphere (Adamic & Glance, 2005) and social media (Bakshy et al., 2015). At the same time, the specific dynamics of polarisation in social networks are the subject of a long debate and are still under scrutiny (e.g. Bail et al., 2018; Barberà et al., 2015; Conover et al., 2011; Nyhan et al., 2023; Wagner et al., 2021). Under certain conditions, polarisation can even be beneficial to democracy (McCoy et al., 2018; Wagner et al., 2021). Alongside this complexity, which still needs to be fully unravelled, several research gaps have been highlighted, including challenges in measuring and differentiating types of polarisation, such as ideological, affective, and interactional (Bruns et al., 2023), understanding depolarisation processes and broadening the scope of research (Kubin & Sikorski, 2021).
Social network analysis can facilitate understanding the intricacies of polarisation with diverse methods, such as network visualisation, community detection algorithms, structural measures, dynamic network analysis, and simulation (Baldassarri & Bearman, 2007). Computational social science methods, such as computational content and sentiment analysis, can complement these approaches by offering insights into the emotional and thematic dimensions of the content that shapes these networks.
For this Network Science special issue, we are looking for papers examining polarisation in social networks. Both methodologically focused work, proposing new methods or comparing existing methods, and novel substantive applications are welcome.
Aims
We expect this special issue to provide an up-to-date interdisciplinary reference for scholars studying polarisation in social networks. This issue will provide an overview on:
- Current methods to quantify polarisation in social networks.
- Selected empirical results investigating polarisation in different contexts (e.g. different social media platforms and online communities).
- Opportunities and limitations in network polarisation research.
- Current research directions.
Timetable
Prospective authors are encouraged to send an abstract to the editors by October 31, 2024, to receive feedback about the suitability of the proposal for the special issue, and to submit the full paper when ready, with a submission deadline of March 31, 2025.
Articles will be published on a FirstView basis - i.e., made available as soon as possible after acceptance. As of 2025, Network Science is adopting a continuous model of publication, and all accepted articles will be curated in a special collection (virtual special issue) in the journal at the end of 2025 / beginning of 2026 with an introduction from the editors.
How to Submit
Authors should consult the Preparing your Materials section of the Network Science website for full guidance, but a quick guide follows:
- Network Science operates a double-blind review process so authors should prepare an anonymised manuscript and a title page;
- LaTeX and Overleaf templates are available for authors who wish to use them;
- We encourage authors to make replication materials available (see the journal's Research Transparency policy);
- Please remember to include a competing interest statement, funding statement and data availability statement in your title page;
- Submit using the NWS Manuscript Central site and select 'Polarisation in Social Networks' from the special issue question in the submission form.
Authors can contact the journal's Managing Editor via nws@cambridge.org with any questions about the submission process.
Open Access
As of 2024, Network Science publishes all articles on an open access basis: freely available to read and redistribute, under a Creative Commons licence.
All authors accepted for publication are published on an open access basis, irrespective of their funding situation or affiliation.
Open Access at Cambridge University Press (CUP) is funded through a mixture of sources:
- Most Network Science (NWS) authors are covered by Transformative Agreements that CUP holds with over 2,700 institutions worldwide.
- If an article has a corresponding author based at one of these institutions, the cost of publishing open access is covered by the agreement. This means that there is no need for the authors to find separate funds to pay an article processing charge.
- NWS authors who are not covered by Transformative Agreements but have funding available from a grant or body that specifically budgets for open access publication are expected to pay an article processing charge (see APC level here).
- NWS without a Transformative Agreement or funding will be able to obtain a discretionary waiver after acceptance.
In summary, we would like to stress that because of the extensive list of agreements - and the availability of waiver processes for authors without funding or an agreement - there is no financial barrier to publication. Authors should feel free to submit irrespective of where they are based or their funding situation.
Editors
- Luca Rossi (IT University Copenhagen), Guest Editor. Email: lucr@itu.dk.
- Matteo Magnani (Uppsala University, Sweden), NWS Editorial Board. Email: matteo.magnani@it.uu.se.
References
Adamic, L. A., & Glance, N. (2005, August). The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery (pp. 36-43).
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115
Baldassarri, D., & Bearman, P. (2007). Dynamics of political polarization. American Sociological Review, 72(5), 784-811.
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber? Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620
Bruns, A., Bechmann, A., Charquero-Ballester, M., Walter, J. G., Stromer-Galley, J., McKernan, B., ... & Vilkins, S. (2023). REVISITING KEY CONCEPTS IN DIGITAL MEDIA RESEARCH: INFLUENCE, POPULISM, PARTISANSHIP, POLARISATION. AoIR Selected Papers of Internet Research.
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2011). Political Polarization on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), Article 1. https://doi.org/10.1609/icwsm.v5i1.14126
Dewey, J. (1927). The public and its problems. Athens, OH: Ohio University Press.
Hohmann, M., Devriendt, K., & Coscia, M. (2023). Quantifying ideological polarization on a network using generalized Euclidean distance. Science Advances, 9(9).
Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American journal of political science, 59(3), 690-707.
Kubin, E., & von Sikorski, C. (2021). The role of (social) media in political polarization: a systematic review. Annals of the International Communication Association, 45(3),188-206.
McCoy, J., Rahman, T., & Somer, M. (2018). Polarization and the global crisis of democracy: Common patterns, dynamics, and pernicious consequences for democratic polities. American Behavioral Scientist, 62(1), 16-42.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444.
Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barberá, P., Chen, A. Y., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Guess, A. M., Kennedy, E., Kim, Y. M., Lazer, D., Malhotra, N.,Moehler, D., … Tucker, J. A. (2023). Like-minded sources on Facebook are prevalent but not polarizing. Nature, 620(7972), 137–144. https://doi.org/10.1038/s41586-023-06297-w
Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. London: Penguin.
Sunstein, C. R. (2001). Republic. com. Princeton University Press.
van Dijk, J. A. G. M., & Hacker, K. L. (2018). Internet and Democracy in the Network Society. Routledge.
Wagner, M. (2021). Affective polarization in multiparty systems. Electoral Studies, 69, 102199. https://doi.org/10.1016/j.electstud.2020.102199