Arctic disinformation on X (Twitter) – an empirical investigation
Bibliographic record
Abstract
Disinformation campaigns have been deployed on social media by foreign states to target democratic elections as well as sow distrust in traditional authority figures. Scarce attention has been devoted to study the intensity and nature of online disinformation in relation to Arctic issues. This article presents evidence from an empirical study that gathered 1.7 million messages posted on the social media X (Twitter) that addressed Arctic issues. In total, we manually coded 12 500 to detect if and how disinformation was present. We found that Arctic disinformation is first and foremost climate disinformation: climate deniers or minimizers are the main drivers of disinformation on the region. These accounts have used common strategies including cherry-picking data, providing anecdotal evidence and attacking scientists to push their arguments forward. Finally, we observed an increase in Arctic disinformation since Elon Musk expressed his intention to acquire the platform.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".