Internet Search Output Propagates Climate Change Sentiment
Bibliographic record
Abstract
Climate change is one of the most threatening and complex problems facing society. A critical step in tackling this crisis is raising climate awareness around the world. Despite the ubiquitous role Internet search engines have in information acquisition, little is known about how such algorithms portray climate change to different communities, and how these portrayals impact climate sentiment. In a sample of 47 countries, we found that preexisting nationwide climate change concern predicted the emotionality of climate change Google Image Search outputs, as rated by a sample of 388 online workers. Moreover, the more emotional these image outputs, the more climate action support they elicited. In a follow-up experiment we then randomly assigned another sample of 700 online workers to receive the climate change image outputs resulting from searches conducted in countries high (e.g., Venezuela; High Concern Condition) or low in preexisting climate change concern (e.g., Kazakhstan; Low Concern Condition), and found that participants in the High Concern Condition increased their climate policy support more than participants in the Low Concern Condition, suggesting a cycle of climate sentiment propagation facilitated by Internet Search algorithms. We discuss the implications of these findings for systemic climate action interventions.
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.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".