Topic modelling of public Twitter discourses, part bot, part active human user, on climate change and global warming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twitter is a key site for understanding the highly polarized and politicized debate around climate change. We examined large datasets comprising about 15 million tweets from different parts of the world referencing climate change and global warming. Our examination of the twenty most active users employing the term ‘global warming’ are likely to be automated accounts or bots than the most active users employing the term ‘climate change’. We used a mixed method approach including topic modelling, which is a digital method that automatedly identifies the top topics using an algorithm to understand how Twitter users engage with discussions on ‘climate change’ and ‘global warming’. The percentage of the top 400 users who use the term ‘climate change’ and believe it is human-made or anthropogenic (82.5%) is much higher than users who use the term ‘global warming’ and believe in human causation (25.5%). Similarly, the percentage of active users who use the term ‘global warming’ were much more likely to believe it is a results of natural cycles (18%) than active users who use the term ‘climate change’ (5%). We also identified and qualitatively analysed the positions of the most active users. Our findings reveal clear politically polarized views, with many politicians cited and trolled in online discussions, and significant differences reflected in terminology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it