Separating Treasure from Trash: Quantifying Data Waste in App Reviews.
Bibliographic record
Abstract
User generated app reviews provide valuable information to multiple stakeholders, however, the exploding number of reviews creates practical business and environmental problems. We propose a new approach for making reviews more environmentally sustainable - reducing waste at source. Many app reviews are ‘trash’, containing little or no potential information value. Alternatively, some reviews are ‘treasures’ providing actionable information. Using these definitions, we develop an automated method to distinguish between trash and treasure app reviews in the Google Play entertainment app category. We find 15% of app reviews are pure trash, while only 26% represent true treasure. Reducing trash reviews at source across all app categories in four major app stores could result in reductions of CO2 emissions ranging from 128 kg to 608 kg depending on the waste-reduction approach adopted. We offer suggestions for eliminating waste at source, including changes to the review interface and acceptance process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".