CRICOS No.00213J
Destructive Polarisation in Climate Debates:
An Exploration Using the Practice Mapping
Approach
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
a.bruns@qut.edu.au
Bluesky: @snurb.info | Mastodon: @snurb@aoir.social | Xitter: @snurb_dot_info
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Image: Midjourney
Polarisation
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(https://www.pewresearch.org/politics/2017/10/05/1-partisan-divides-over-political-values-widen/)
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(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/)
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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…)
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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):
a) breakdown of communication;
b) discrediting and dismissing of information;
c) erasure of complexities;
d) exacerbated attention and space for extreme voices;
e) exclusion through emotions.
Image: Midjourney
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(It’s complicated.)
Assessing Polarisation
Image: Midjourney
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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
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• From this…
(blue: retweets / red: @mentions)
• Not to this…
• But to this…
Interaction Networks Are Not Enough
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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 …)
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• Before: • After:
What We Aim For…
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Vectorising Account Practices
Data
Preparation
For each attribute,
format data as:
post_id,
account_id,
activity_type
Vector
Aggregation
Turn per-post data into
per-account activity
vectors:
account_id,
activity_vector
(normalised)
Vector
Comparison
Systematically compare
activity vectors for each
pair of accounts (using
cosine similarity):
account_1,
account_2,
cosine_similarity
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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
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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
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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"
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
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Summer 2019/20
Bushfires
February 2022
Brisbane Floods
October 2022
NSW/Victoria Floods
March 2021
NSW Floods
December 2023
Cyclone Jasper
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Summer 2019/20
Bushfires
climate debate and Morrison government inaction
C
O
V
ID
-1
9
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Work in Progress:
Dynamic Practice
Mapping
Cluster Centroids
Selection: Nodes with highest similarity (weighted degree) within home cluster
Cluster Centroids
Selection: Nodes with highest similarity (weighted degree) within home cluster
Centroids and Key Accounts
Selection: Centroids + 200 most active pages in the dataset
Dynamic visualisation: practice similarities during sliding two-month window
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2019: Jan.-Feb.
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2019: Mar.-Apr.
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2019: May-June
Federal election:
18 May 2019
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2019: July-Aug.
Post-election
debates
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2019: Sep.-Oct.
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2019: Nov.-Dec.
Summer 2019/20
cushfire crisis
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2020: Jan.-Feb.
Bushfire crisis
and aftermath
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2020: Mar.-Apr.
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2020: May-June
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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
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Further Outlook
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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?
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Thank you
Image: Midjourney
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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

Destructive Polarisation in Climate Debates: An Exploration Using the Practice Mapping Approach

  • 1.
    CRICOS No.00213J Destructive Polarisationin Climate Debates: An Exploration Using the Practice Mapping Approach 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 a.bruns@qut.edu.au Bluesky: @snurb.info | Mastodon: @snurb@aoir.social | Xitter: @snurb_dot_info
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  • 3.
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  • 5.
  • 6.
  • 7.
    CRICOS No.00213J Forms ofPolarisation • 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…)
  • 8.
    CRICOS No.00213J 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): a) breakdown of communication; b) discrediting and dismissing of information; c) erasure of complexities; d) exacerbated attention and space for extreme voices; e) exclusion through emotions. Image: Midjourney
  • 9.
  • 10.
    CRICOS No.00213J Practice: the sumtotal 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
  • 12.
    CRICOS No.00213J • Fromthis… (blue: retweets / red: @mentions) • Not to this… • But to this… Interaction Networks Are Not Enough
  • 13.
    CRICOS No.00213J When SocialNetwork 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 …)
  • 14.
    CRICOS No.00213J • Before:• After: What We Aim For…
  • 15.
    CRICOS No.00213J Vectorising AccountPractices Data Preparation For each attribute, format data as: post_id, account_id, activity_type Vector Aggregation Turn per-post data into per-account activity vectors: account_id, activity_vector (normalised) Vector Comparison Systematically compare activity vectors for each pair of accounts (using cosine similarity): account_1, account_2, cosine_similarity
  • 16.
    CRICOS No.00213J Trouble withFacebook • 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
  • 17.
    CRICOS No.00213J Dataset: postsfrom 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
  • 18.
    CRICOS No.00213J 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"
  • 19.
    Nodes: public Facebookpages 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
  • 20.
    CRICOS No.00213J Summer 2019/20 Bushfires February2022 Brisbane Floods October 2022 NSW/Victoria Floods March 2021 NSW Floods December 2023 Cyclone Jasper
  • 21.
    CRICOS No.00213J Summer 2019/20 Bushfires climatedebate and Morrison government inaction C O V ID -1 9
  • 22.
  • 23.
    CRICOS No.00213J Work inProgress: Dynamic Practice Mapping
  • 24.
    Cluster Centroids Selection: Nodeswith highest similarity (weighted degree) within home cluster
  • 25.
    Cluster Centroids Selection: Nodeswith highest similarity (weighted degree) within home cluster
  • 26.
    Centroids and KeyAccounts Selection: Centroids + 200 most active pages in the dataset
  • 27.
    Dynamic visualisation: practicesimilarities during sliding two-month window
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    CRICOS No.00213J Making Senseof 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
  • 38.
  • 39.
    CRICOS No.00213J 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?
  • 40.
  • 41.
    CRICOS No.00213J This researchis supported by the Australian Research Council through the Australian Laureate Fellowship project Determining the Dynamics of Partisanship and Polarisation in Online Public Debate. Acknowledgments