Google Trends as a Predictor of Presidential Elections: The United States Versus Canada
Bibliographic record
Abstract
The media and election campaign managers conduct several polls in the days leading up to the presidential elections. These preelection polls have a different predictive capacity, despite the fact that under a Big Data approach, sources that indicate voting intention can be found. In this article, we propose a free method to anticipate the winner of the presidential election based on this approach. To demonstrate the predictive capacity of this method, we conducted the study for two countries: the United States of America and Canada. To this end, we analysed which candidate had the most Google searches in the months leading up to the polling day. In this article, we have taken into account the past four elections in the United States and the past five in Canada, since Google first published its search statistics in 2004. The results show that this method has predicted the real winner in all the elections held since 2004 and highlights that it is necessary to monitor the next elections for the presidency of the United States in November 2020 and to have more accurate information on the future results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".