Bibliographic record
Abstract
Online types of expression in the form of social networks, micro-blogging, blogs and rich content sharing platforms have proliferated in the last few years. Such proliferation contributed to the vast explosion in online data sharing we are experiencing today. One unique aspect of online data sharing is tags manually inserted by content generators to facilitate content description and discovery (e.g., hashtags in tweets). In this paper we focus on these tags and we study and propose algorithms that make use of tags in order to automatically organize and categorize this vast collection of socially contributed and tagged information. In particular, we take a holistic approach in organizing such tags and we propose algorithms to partition as well as rank this information collection. Our partitioning algorithms aim to segment the entire collection of tags (and the associated content) into a specified number of partitions for specific problem constraints. In contrast our ranking algorithms aim to identify few partitions fast, for suitably defined ranking functions. We present a detailed experimental study utilizing the full twitter firehose (set of all tweets in the Twitter service) that attests to the practical utility and effectiveness of our overall approach. We also present a detailed qualitative study of our 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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".