Dynamics of Destructive Polarisation in Mainstream and Social Media: The Case of the Australian Voice to Parliament Referendum

Keynote presented at the Symposium 'Indicators of Social Cohesion' at the Research Institute Social Cohesion, Hamburg, 25 Apr. 2024.

1. Dynamics of Destructive Polarisation in Mainstream and Social Media: The Case of the Australian Voice to Parliament Referendum 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] @snurb_dot_info | @[email protected] | @snurb.bsky.social

2. Implies that something is threatening that cohesion… Gesellschaftlicher Zusammenhalt ? Societal Cohesion?

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. 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)

5. 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)

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

7. Image: Midjourney Polarisation

8. 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, 4 + 4* PhD students, 1 data scientist Cross-national comparisons (intended: AU, US, UK, DE, DK, CH, probably + BR, PE, CA) Longitudinal analysis over the course of the project * Starting in 2024 – interested? Get in touch! ( [email protected] )

9. Image: Midjourney Forms of Polarisation

10. (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/)

11. Forms of Polarisation Polarisation at what levels? Micro: between individuals Meso : between groups Macro: across society Mass: involving everyone Elite: amongst formal political actors (however defined) See: Esau et al. (2023) — https://eprints.qut.edu.au/238775/ (and chapter forthcoming in the Routledge Handbook of Political Campaigning )

12. Forms of Polarisation Polarisation on what attributes? 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…)

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

14. 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., 2023 ): breakdown of communication; discrediting and dismissing of information; erasure of complexities; exacerbated attention and space for extreme voices; exclusion through emotions. Image: Midjourney

15. (And also, are these the right / the only symptoms of destructive polarisation?) But: How do we operationalise these symptoms in empirical research?

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

17. 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

18. 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

19. Example: No Campaign’s Use of Fear and Doubt “The campaign to sink the Voice has instructed volunteers to use fear and doubt rather than facts to trump arguments used by the Yes camp.” ( Sydney Morning Herald ) Symptoms of Dysfunction: Exclusion through Emotions ( https://www.smh.com.au/politics/federal/no-campaign-s-fear-doubt-strategy-revealed-20230910-p5e3fu.html )

20. Example: YouTube Videos Shared on Facebook Explicitly conspiracist (and antisemitic) videos amongst the YouTube video content shared most frequently in public Facebook groups and pages. Symptoms of Dysfunction: Disproportionate Attention to Extreme Voices

21. Example: Sky News Australia Reporting Many of the most widely shared videos from influential conservative news source Sky News Australia made explicitly conspiracist claims. Symptoms of Dysfunction: Discrediting and Dismissal of Information ( https://www.skynews.com.au/opinion/peta-credlin/transfer-of-power-voice-has-very-little-to-do-with-supporting-indigenous-australians/video/597252c79e59d25cf3bfe0c423768dc1 , https://www.skynews.com.au/australia-news/sky-news-host-peta-credlin-exposes-labors-lie-on-the-uluru-statement-from-the-heart-under-freedom-of-information-act/news-story/f1539032a44c6658c2feb352b2ddea45 , https://www.skynews.com.au/opinion/andrew-bolt/youve-been-misled-real-agenda-of-the-voice-exposed-in-a-brawl/video/d2255bf53cb4c0223e990cabd1461f14 )

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

23. Example: @mention network on Twitter* Some separation into Yes/No camps, with continued exchanges between the two sides – but often lack of meaningful engagement between them. Symptoms of Dysfunction: Breakdown of Communication * Data obtained through NodeXL Twitter scraper, with limited completeness. Red: exclusively using #VoteNo; green: exclusively using #VoteYes.

24. Can new methods add more rigour to the analysis? ⚠️ Work in Progress… Towards a More Systematic Validation and Diagnosis of These Symptoms?

25. Current thinking: Quantifying specific aspects of individual participant activities, then identifying and interpreting similar patterns at a group level. * With particular thanks to Kateryna Kasianenko. Beyond Qualitative Interpretation: Practice Mapping* Image: Midjourney

26. Twitter @mention network during Voice to Parliament campaign Red: exclusively using #VoteNo; green: exclusively using #VoteYes. Twitter interaction pattern similarity network – based on cosine similarity between normalised interaction vectors per account, colours based on modularity detection. Pro-Voice campaigners Labor supporters Anti-Voice campaigners Liberal / National supporters

27. 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

28. Assessing Destructive Polarisation Key questions: Does practice mapping show distinct practices? What divergent patterns drive such distinctions? Do these patterns map onto one of the symptoms of destructive polarisation? (Or: do they represent a new pattern that might be seen as destructive – a new symptom?) How severe are these differences (i.e. how deeply and destructively polarised is the situation)? How are these patterns evolving over time? Image: Midjourney

29. Thank you! Image: Midjourney

30. 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