Analysis of public awareness on global warming: Forecasting using Google Trends and FB prophet
Bibliographic record
Abstract
Global warming is a world problem that must be solved jointly by all countries in the world. Public awareness of global warming shows a decreasing trend over time, as shown in the global community's search results for the keyword global warming which tends to decrease in number. So, research must be carried out to find out the causes of the decline in global public awareness of this issue. This research aims to predict several keywords related to global warming and try to find the reasons why world public awareness tends to continue to decline. This research uses Google Trends to retrieve the dataset and uses the FB prophet model as a forecasting algorithm in machine learning. The research results show that the trend in people's searches for the keyword "global warming" will tend to decline over the next year. Another finding is that there are contradictory keywords on Google Trends that tend to increase, namely "evidence of global warming" and "why climate change is fake". The MAPE (Mean Average Percentage Error) score for the two contradictory keywords is 0.16 and 0.15. Another finding is, if the search dataset on Google Trends has a high fluctuating number of searches, additional columns can be added to the dataset by using the max function to combine several related keywords to retrieve the highest number of searches. Added max column can increase MAPE score in forecasting results. The MAPE score in the max column is 0.159. Another finding was that contradictory keywords on Google Trends came from South Africa, America, Australia, the Philippines, England, Canada, Vietnam, and India.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".