Climate Change Communication Among European and American Politicians from 2015 to 2020 on X
Bibliographic record
Abstract
Climate change is a multifaceted issue that encompasses environmental, social, cultural, and political dimensions. Greater involvement and awareness have been fostered among online community members thanks to the rise of the internet and the use of social media networks. Among these, X (formerly known as Twitter) stands out as a catalyst for creating shared knowledge, connectedness, and concern among its members, contributing to a greater sense of responsibility. Much attention has been paid to the influence of X on politicians, who use it as a vital political tool to engage with the public, set agendas, and signal policy intentions. This paper delves into an analysis of climate change communication on X by 36 politicians from the EU and the U.S. It seeks to determine whether there is a correlation between language use and geographical location based on the assumption that the European Union has had a more stable and coherent concern about climate change over time, compared to the United States (Wendler, 2022). By means of corpus linguistics and discourse analysis, based on a modern-diachronic approach, multiword expressions extracted from an ad hoc corpus of 163 753 tweets are examined to identify their lexical saliency (Baker, 2006). Despite limitations, this paper is an attempt to shed light on the communicative strategies employed by politicians in addressing climate issues and highlights the need for further investigation into climate change communication on a global scale.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".