Learning to predict trending queries
Bibliographic record
Abstract
Among the many tasks driven by very large scaled web search queries, it is an interesting task to predict how likely queries about a topic become popular (a.k.a. trending or buzzing) as the news in the near future, which is known as "Detecting trending queries." This task is nontrivial since the realization of buzzing trends of queries often requires sufficient statistics through users' activities. To address this challenge, we propose a novel framework that predicts whether queries become trending in the future. In principle, our system is built on the two learners. The first is to learn dynamics of time series for queries. The second, our decision maker, is to learn a binary classifier that determines whether queries become trending. Our framework is extremely efficient to be built taking advantage of the grid architecture that allows to deal with the large volume of data. In addition, it is flexible to continuously adapt as trending patterns evolve. The experiments results show that our approach achieves high quality of accuracy (over 77.5%} true positive rate) and yet detects much earlier (on average 29 hours advanced) than that of the baseline system.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".