Destructive Polarisation in Climate Debates: An Exploration Using the Practice Mapping Approach
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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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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…)
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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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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
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• Fromthis…
(blue: retweets / red: @mentions)
• Not to this…
• But to this…
Interaction Networks Are Not Enough
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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 …)
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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
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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
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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
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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
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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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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