Investigating the Dynamics of Destructive Polarisation in Public Communication

Invited seminar at the Brussels Institute for Advanced Studies, 15 Sep. 2025.

1. Investigating the Dynamics of Destructive Polarisation in Public Communication Axel Bruns with important contributions from: Australian Laureate Fellow Laura Vodden Katharina Esau Sebastian Svegaard Digital Media Research Centre Tariq Choucair Samantha Vilkins Kate O’Connor Farfan Queensland University of Technology Laura Lefevre Vishnu PS Carly Lubicz-Zaorski Brisbane, Australia Ehsan Dehghan Kateryna Kasianenko [email protected] Bluesky: @snurb.info | Mastodon: @[email protected] | Xitter : @snurb_dot_info

2. Filter Bubbles? Echo Chambers? No: Polarisation – Constructive or Destructive Practice Mapping to Detect Symptoms Roadmap

3. Can we simply blame our platforms and their algorithms? Filter bubbles? Echo chambers? (https://commons.wikimedia.org/wiki/File:Eli_Pariser,_author_of_The_Filter_Bubble_-_Flickr_-_Knight_Foundation.jpg)

4. Bubble Trouble Echo Chambers? Filter Bubbles? Where exactly? General search engines News search engines, portals, and recommender systems Social media (but where – profiles, pages, hashtags, groups …?) What exactly? Hermetically sealed information enclaves full of misinformation? Self-reinforcing ideological in-groups of hyperpartisans ? Politically partisan communities of any kind? Why exactly? Ideological and societal polarisation amongst citizens? Algorithmic construction of distinct and separate publics? Feedback loop between the two? Defined how exactly? Argument from anecdote and ‘common sense’, rather than empirical evidence Promoted by non-experts ( Sunstein : legal scholar; Pariser : activist and tech entrepreneur) Image: Midjourney

5. Echo Chambers and Filter Bubbles in Social Media Early blogosphere studies: Strong U.S. focus Polarisation and ‘mild echo chambers’ E.g. Adamic & Glance (2005) Social media studies: Especially Twitter, less Facebook or other platforms Hashtag / keyword datasets Mixed results: Significant distinctions between @mention, retweet, follow networks And between lead users and more casual participants Adamic & Glance (2005)

6. No. Just No. No evidence for ‘hard’ echo chambers / filter bubbles Success of mis- and disinformation campaigns depends on their absence Hyperpartisan activists actively seeking out enemies and their content Social media are only one part of a more diverse media mix (and themselves very diverse) Most people simply don’t care enough about news and politics to get locked in ‘Mild’ echo chambers / filter bubbles are an oxymoron Mild selective attachment is nothing new, and doesn’t prevent encounters with diverse views We already have names for this: communities of interest, counterpublics , parasitic publics Yes, these groups can be deeply problematic – but not because they’re echo chambers We must confront the underlying issues, not blame technology for societal problems (Yes, there are serious problems with social media platforms. No, not these ones.)

7. Williams, H. T. P., McMurray, J. R., Kurz, T., & Lambert, F. H. (2015). Network Analysis Reveals Open Forums and Echo Chambers in Social Media Discussions of Climate Change. Global Environmental Change , 32 , 126–138. http://doi.org/10.1016/j.gloenvcha.2015.03.006

8. Connectivity, Not Disconnection Image: Midjourney

9. Ready access to information enables spread of ‘fake news’, hyperpartisanship, and polarisation. (But also social connection and community support.) Hyperpartisans, Hyperconnected (https://twitter.com/bigfudge212121/status/1259317174776115201)

10. The problem with an extraterrestrial -conspiracy mailing list isn’t that it’s an echo chamber; it’s that it thinks there’s a conspiracy by extraterrestrials . — David Weinberger, Salon , 21 Feb. 2004 (https://commons.wikimedia.org/wiki/File:David_Weinberger.jpg)

11. (https://www.vice.com/de/article/pam5nz/deshalb-ist-filterblase-die-blodeste-metapher-des-internets)

12. Frankfurter Allgemeine Sonntagszeitung , 25 May 2025

13. Image: Midjourney Polarisation

14. Our Project Australian Laureate Fellowship (2022-27): Determining the Drivers and Dynamics of Partisanship and Polarisation in Online Public Debate Digital Media Research Centre, Queensland University of Technology, Brisbane, Australia 4 postdocs, 10 PhD students, 1 data scientist Cross-national comparisons, case studies, longitudinal analysis Enabled by methods development

15. Image: Midjourney Forms of Polarisation

16. (https://www.pewresearch.org/politics/2017/10/05/1-partisan-divides-over-political-values-widen/)

17. (https://www.pewresearch.org/politics/2022/08/09/as-partisan-hostility-grows-signs-of-frustration-with-the-two-party-system/pp_2022-08-09_partisan-hostility_01-08/) (https://www.pewresearch.org/politics/2023/09/19/the-republican-and-democratic-parties/pp_2023-09-19_views-of-politics_04-02/)

18. Forms of Polarisation Polarisation at what level? Issue-based: disagreements over specific policy settings Ideological: fundamental differences based on political belief systems Affective: political beliefs turned into deeply felt in-group / out-group identity Perceived: view of society, as based on personal views and media reporting Interpretive: reading of issues, events, and media coverage based on personal views Interactional: manifested in choices to interact with or ignore other individuals/groups (and more…)

19. Image: Midjourney A Problem? (When?)

20. Agonism? Polarisation? Dysfunction? How bad is it, exactly? All politics is polarised (just not to the point of dysfunction) Much ( most ?) politics is multipolar, not just left/right When does mild antagonism turn into destructive polarisation? We suggest five symptoms ( Esau et al., 2024 ): breakdown of communication; discrediting and dismissing of information; erasure of complexities; exacerbated attention and space for extreme voices; exclusion through emotions. Image: Midjourney

21. Practice : the sum total of each account’s actions and interactions – its patterns of engagement with other accounts, its use of language, its sharing of URLs, images, and videos, etc. Practice Mapping

22. Social Network Analysis beyond Twitter The Golden Age of network data is over (for now?): Social network analysis mostly meant Twitter network analysis Data on networked interactions not widely available for Facebook, Instagram, … No real networks of interaction on Reddit, YouTube, TikTok, …: threads , not networks Communities, not network clusters – that means attention to content , too: Networks very often a tool for finding clusters and communities with similar practices Those practices include activities other than direct interaction with each other Communities defined by shared language, identities, beliefs, values, ideas, sources, … How do we identify such communities in contemporary social media platforms?

23. Image: Midjourney End of an Era … … for Network Analysis

24. Trouble with Facebook Conventional network mapping fails: Data on public pages / public groups only (from CrowdTangle / Meta Content Library) Very limited data on direct or indirect networked interactions Practice mapping draws on other attributes: Similarities in link sharing (external domains) Similarities in on-sharing (posts from other public pages / groups) Similarities in video sharing (specific YouTube videos) Similarities in language choices (via word embeddings of posts)  Network of similarities between Facebook spaces Image: Midjourney

25. Account-to-account interactions (relative to interactive affordances available on any given social media platform) Account’s post content (topics, sentiment, hashtags, named entities, etc.) Account’s use of sources (URLs, domains, embedded videos and images, etc.) Account’s profile information (name, description, etc.) Manually and computationally coded information about the account and its posts … Potential Patterns to Operationalise in Practice Mapping Image: Midjourney

26. Vectorising Account Practices

27. Case Study ( https://www.abc.net.au/news/2023-10-08/voice-polls-show-support-lower-than-republic-vote/102942468 )

28. Indigenous rights and recognition: Complex topic since European arrival in 1788 Indigenous Australians remain severely disadvantaged Persistent lack of formal consultation Voice to Parliament: Endorsed in 2017 Ulu r u Statement from the Heart Commitment to referendum on a Voice in Anthony Albanese’s 21 May 2022 election victory speech Referendum design revealed in March 2023 Constitutional referendum held on 14 Oct. 2023 Proposed Constitutional Amendment: Chapter IX Recognition of Aboriginal and Torres Strait Islander Peoples 129 Aboriginal and Torres Strait Islander Voice In recognition of Aboriginal and Torres Strait Islander peoples as the First Peoples of Australia: There shall be a body, to be called the Aboriginal and Torres Strait Islander Voice; The Aboriginal and Torres Strait Islander Voice may make representations to the Parliament and the Executive Government of the Commonwealth on matters relating to Aboriginal and Torres Strait Islander peoples; The Parliament shall, subject to this Constitution, have power to make laws with respect to matters relating to the Aboriginal and Torres Strait Islander Voice, including its composition, functions, powers and procedures. Case Study: Voice to Parliament Referendum

29. Referendum Vote Voting modus: Compulsory for all registered voters Actual turnout: 89.95% Requirements for success: Majority of voters overall Majority of voters in majority of states (4 of 6) Results: Overall: 40% Yes, 60% No 0 of 6 states Yes win only in Australian Capital Territory By Teratix - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=131601888

30. Example: Liberal Party ‘No’ Campaign on Instagram Simple language, appeals to ignorance. Symptoms of Dysfunction: Erasure of Complexities ( https://www.instagram.com/p/CyMy7hFI1Kw/ )

31. Dataset: posts about the Voice to Parliament constitutional referendum in Australia, from public Facebook pages and groups (1 Jan. to 13 Oct. 2023) Practice attributes: domains shared, YouTube videos shared, posts on-shared, language choices Voice to Parliament

32. Sky News Australia No Campaigners Anti-LNP ABC Pages and On-Sharers Uluru Statement from the Heart Yes23 Community Organisations SBS Pages and On-Sharers YES NO (agonistic discursive alliance) (antagonists) One Nation Yes Campaigners, Labor Party, Unions Local Campaigns NITV Nodes: public Facebook pages and groups addressing the referendum Node size: volume of posts (spline applied), minimum 3 posts Node colour: Louvain modularity algorithm cluster detection Edge weights: domain sharing similarity + YouTube sharing similarity + on-sharing similarity + Vertex AI text embedding similarity

33. Zero-shot classification of post content (following Laurer et al., 2023)

34. Making Sense of Practice Patterns Key questions: Does practice mapping show distinct practices? What divergent patterns drive such distinctions? Do clusters represent communities of practice? How severe are the differences in practices? How are these patterns evolving over time? Should we interpret them as symptoms of destructive polarisation ? Image: Midjourney

35. Thank you Image: Midjourney

36. This research is supported by the Australian Research Council through the Australian Laureate Fellowship project Determining the Dynamics of Partisanship and Polarisation in Online Public Debate . Acknowledgments