“Friends” of Science? Critically analysing the multimodal discourse of a long-standing climate denial front group on Instagram
Bibliographic record
Abstract
Problematic climate information affects public support for mitigation policies. Frequently distributed through a powerful coalition of contrarian actors—the climate change counter movement (CCCM)—it undermines scientific evidence to protect vested interests through ongoing promotional campaigns. On social media, image-based climate misinformation often outperforms verified content. Instagram, with its multimodal vernacular, is a crucial platform for climate misinformation research. In this paper, a novel, visual-first exploratory method combines unsupervised machine learning with multimodal critical discourse analysis. I employ a custom computational tool, the ‘Image Machine, to identify dominant visual signatures and discursive themes in a dataset of 15,000 Instagram posts, collected via hashtags representing climate denial discourse.Cluster analysis reveals a distinct visual signature that represents the branding logics of established contrarian front group, Friends of Science Society (FoS). Through multimodal critical discourse analysis of a visual cluster and representative post case study, this research interrogates the discursive strategies and climate denial rhetoric of FoS that—through deliberate intertextual, multimodal tactics that exploit Instagram’s affordances—aim to confound public climate science knowledge. Findings situate FoS firmly in the role of front group within the denial machine, instrumentally using climate denial as a rhetorical and discursive strategy to support Canada’s fossil fuel interests.Funded by fossil interests, FoS exploits signature visuality to solidify contrarian narratives and reinforce its brand. In documenting individuals and organisations implicit in the FoS funding structure, this research contributes to scholarship about the CCCM, as well introduces a novel methodology to ascribe multimodal—particularly, visual—means for detecting problematic climate information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".