Twitter’s Fake News Discourses Around Climate Change and Global Warming
Bibliographic record
Abstract
In this empirical study, we collected about 6.8 million tweets that mentioned “fake news”, and we extracted references to climate change and/or global warming to understand the public discourses around these two issues. Using a mixed method, the study’s findings show that there is a clear politically polarized discussion on climate change. We found that the majority of tweets focus on the United States context though references to other Western coutnries are often made. The anti-Liberal or anti-Democratic online community was more active on Twitter than the anti-conservative or anti-Republican community. Also, more than half the examined most retweeted posts contained claims about climate change being a natural cycle or even denying it exists, while about a third of these tweets stated that climate change was anthropogenic. The implications of the study are discussed, we argue that fake news as a term has a hollow meaning as it is used as a buzzword to discredit opponents and further the political agenda of different parties not only in the United States but also in other Western countries like Australia.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".