Tracking Shifts in Discursive Alliances: A Longitudinal Analysis of Australian Climate Change Discourses on Facebook through Practice Mapping

Paper by Axel Bruns, Carly Lubicz-Zaorski, Tariq Choucair, Laura Vodden, and Ehsan Dehghan, presented at the 2025 Association of Internet Researchers (AoIR) conference in Niterói, Rio de Janeiro, 18 Oct. 2025.

1. Tracking Shifts in Discursive Alliances: A Longitudinal Analysis of Australian Climate Change Discourses on Facebook through Practice Mapping Axel Bruns, Carly Lubicz -Zaorski, Tariq Choucair, Laura Vodden, Ehsan Dehghan Digital Media Research Centre Queensland University of Technology Brisbane, Australia Bluesky: @snurb.info | Mastodon: @[email protected] | Xitter : @snurb_dot_info

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3. 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

4. (It’s complicated.) Assessing Polarisation Image: Midjourney

5. 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

6. Vectorising Account Practices

7. 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) Similarities in specific keyword choices (from pre-defined list)  Network of similarities between Facebook spaces Image: Midjourney

8. Dataset: posts from Australian Facebook pages, containing climate-related keywords (1 Jan. 2018 to 10 Aug. 2024) Practice attributes: domains shared, YouTube videos shared, posts on-shared, language choices (via word embeddings), keywords used Australian Climate Change Debates

9. Keywords "climate", "climatic", "global warming", "global heating", "planetary crisis", "extreme weather", "weather event", "weather catastrophe", "weather emergency", "severe weather", "natural disaster", "natural disasters", "environmental impact", "environment impact", "environmental impacts", "environment impacts", "environmental policy", "environmental policies", "environment policy", "environment policies", "nature policy", "sustainability", " anthropocene ", "heat", "heatwave", "heatwaves", "extreme temperature", "extreme temperatures", "cyclone", "cyclonic", "cyclones", "rain", "raining", "rains", "rained", "flood", "floods", "flooding", "flooded", "hurricane", "hurricanes", "hail", "hailing", "hailed", "hails", "hailstorm", "hailstorms", "inundation", "inundations", "inundated", "inundating", "inundates", "landslide", "landslides", "bushfire", "bushfires", "bush fire", "bush fires", "forest fire", "forest fires", " forrest fire", " forrest fires", "wild fire", "wild fires", "wildfire", "wildfires", "backburning", "backburn", "back-burn", "back-burning", "back burn", "back burning", "storm", "storms", "blizzard", "blizzards", "snowstorm", "snowstorms", "drought", "droughts", "tornado", "tornados", "tornadoes", "monsoon", "monsoons", "typhoon", "typhoons", "biodiversity", "ecosystem", "ecosystems", "coral", "corals", "reef", "reefs", "extinction", "extinctions", "species loss", "habitat loss", "ecocide", "hydro energy", "hydrogen energy", "hydro power", "hydropower", "hydroelectric energy", "hydroelectric power", "hydro electric power", "hydro electric energy", "green energy", "clean energy", "clean hydrogen", "green hydrogen", "wind farm", "wind farms", "windfarm", "windfarms", "wind power", "wind energy", "wind turbine", "offshore wind", "onshore wind", "solar farm", "solar farms", "solar power", "solar energy", "blackout", "blackouts", "brownout", "brownouts", "energy grid", "energy grids", "energy policy", "energy generation", "renewable", "renewables", "energy transition", "green transition", "green economy", "sustainable economy", "circular economy", "nuclear energy", "nuclear power", "coal energy", "coal power", "coal fired", "coal-fired", "brown coal", "lignite", "clean coal", "shale oil", "shale energy", "fracking", "frack", "natural gas", "biogas", "petroleum", "crude oil", "biofuels", "biofuel", " paris agreement", " paris accord", " paris agreements", " paris accords", " el nino ", " el niño ", "la nina ", "la niña ", " indian ocean dipole", "antarctica", " antarctic ", "arctic", " greenland ", "deforestation", "reforestation", "afforestation", "reforest", "deforest", "afforest", "deforesting", "reforesting", "afforesting", "land clearing", "desertification", "sea level", "sea levels", "ocean level", "ocean levels", "ocean temperature", "ocean temperatures", "ocean acidity", "ocean acidification", "permafrost", "glacier", "glacial", "glaciers", "glaciation", "melting ice", "ice cap", "icecap", "global melting", "coral bleaching", "emissions", "fossil fuel", "fossil fuels", "fossil energy", "carbon", "decarbonisation", "decarbonization", "decarbonised", "decarbonized", "decarbonize", "decarbonise", "greenhouse gas", "GHG", "greenhouse effect", "CO2", "CO-2", "CO2", "CH4", "CH4", "CH-4", "ozone layer", "ozone hole", "net zero", "net-zero", "green steel", "methane", "Fridays for Future", "#FFF", "School Strike for Climate", "School Strike 4 Climate", "#SS4C", "Extinction Rebellion", "#XR", "Greta Thunberg", "# ClimateStrike ", " netzero ", "net-zero", "critical minerals", "critical energy minerals", "green metals", "green iron", "green steel", "low carbon fuel", "low carbon liquid fuels", "low-carbon liquid fuels", "pumped hydro", "snowy hydro", "Snowy 2.0", "gas strategy", "gas industry", "low-emissions gas", "gas fields", " gasfields ", "liquefied natural gas", "carbon credits", "carbon credit", "carbon offset", "carbon offsets", "carbon abatement", "renewable hydrogen", "emission reduction", "emissions reduction", "climate wars", "reckless renewables", "carbon tax", " ute tax", "baseload power", "power generation", "electricity generation", "community batteries", "home batteries", "National Battery Strategy", "renewable energy", "wind turbines", "decarbonise", "decarbonize", "nuclear", "small modular reactor", "small modular reactors", "micro modular reactors", "micro modular reactor", "nuclear-powered"

10. Nodes: public Facebook pages Node size: volume of posts (spline applied), minimum 200 posts Node colour: Louvain modularity algorithm cluster detection Edge weights: pairwise comparison of similarities for domain + YouTube + on-sharing + word embeddings + keyword practices climate policy weather chasing mainstream news emergency services political news and commentary weather renewables rural news NRL / Melbourne Storm sustainability House of Heat / Brisbane Heat Great Barrier Reef NSW Rural Fire Service offroad / boating / camping

11. Summer 2019/20 Bushfires February 2022 Brisbane Floods October 2022 NSW/Victoria Floods March 2021 NSW Floods December 2023 Cyclone Jasper

12. Summer 2019/20 Bushfires climate debate and Morrison government inaction COVID-19

13. Work in Progress: Dynamic Practice Mapping

14. Cluster Centroids Selection: Nodes with highest similarity (weighted degree) within home cluster

15. 2019: Jan.-Feb.

16. 2019: Mar.-Apr.

17. 2019: May-June Federal election: 18 May 2019

18. 2019: July-Aug. Post-election debates

19. 2019: Sep.-Oct.

20. 2019: Nov.-Dec. Summer 2019/20 cushfire crisis

21. 2020: Jan.-Feb. Bushfire crisis and aftermath

22. 2020: Mar.-Apr.

23. 2020: May-June

24. Making Sense of Practice Patterns Key questions: Does practice mapping show distinct practices? What divergent patterns drive such distinctions? Who are the key actors in these clusters? 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

25. Further Outlook

26. Next Steps Improve dynamic analysis and visualisation More targeted selection of accounts of interest Dynamic changes to specific practice components over time (URLs, language, keywords…) Cross-referencing with external events over the period of analysis More targeted analysis Focus on the politics / policies / news coverage clusters Dedicated dynamic practice mapping Breakdown of large overall clusters into more specific subsets Discursive alliances? Are they present here? Do they change over time? If so, what prompts this? How does this compare – are climate debates more/less dynamic than other topics?

27. Thank you Image: Midjourney

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29. 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