Mining the Temporal Statistics of Query Terms for Searching Social Media Posts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an emerging consensus that time is an important indicator of relevance for searching streams of social media posts. In a process similar to pseudo-relevance feedback, the distribution of document timestamps from the results of an initial query can be leveraged to infer the distribution of relevant documents, for example, using kernel density estimation. In this paper, we explore an alternative approach to mining relevance signals directly from the temporal statistics of query terms in the collection, without the need to perform an initial retrieval. We propose two approaches: a linear ranking model that combines features derived from temporal collection statistics of query terms and a regression-based method that attempts to directly predict the distribution of relevant documents from query term statistics. Experiments on standard tweet test collections show that our proposed methods significantly outperform competitive baselines. Furthermore, studies of different feature combinations show the extent to which different types of temporal signals impact retrieval effectiveness.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it