Diagnosing Destructive Polarisation in Public Discourse: The Practice Mapping Framework

Paper by Axel Bruns, Katharina Esau, Kateryna Kasianenko, Tariq Choucair, and Vish Padinjaredath Suresh, presented at the ZeMKI 20th Anniversary Conference, Bremen, 23 Oct. 2025.

1. Diagnosing Destructive Polarisation in Public Discourse: The Practice Mapping Framework Axel Bruns , Katharina Esau, Kateryna Kasianenko, Tariq Choucair, Vish Padinjaredath Suresh Digital Media Research Centre Queensland University of Technology Brisbane, Australia [email protected] Bluesky: @snurb.info | Mastodon: @[email protected] | Xitter : @snurb_dot_info

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

4. 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)

5. 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.)

6. 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)

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

8. Image: Midjourney Polarisation

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

10. 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…)

11. 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

12. 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

13. From this… ( blue: retweets / red: @mentions) Not to this… But to this… Interaction Networks Are Not Enough

14. When Social Network Analysis Fails… What’s the problem? Difficulty in combining various multi-modal interactions into one graph: E.g. @mentions, @replies, retweets, quote tweets, follower relationships, … Difficulty in representing directionality: E.g. distinguishing between reciprocal and non-reciprocal @replies, retweets, … Difficulty in interpreting ‘community detection’ results: Popular algorithms may ignore directionality / reciprocality Clusters of interconnected accounts are not necessarily actual communities (… and more …)

15. Before: After: What We Aim For…

16. Vectorising Account Practices

17. 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

18. 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

19. 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

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

21. 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

22. Thank you Image: Midjourney

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24. 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