Using Machine Learning to Improve the Sustainability of the Online Review Market
Bibliographic record
Abstract
Powered by the Internet, the online review market has grown exponentially, providing a trove of information to customers and business. However, cracks have started to appear around the economic, social and environmental sustainability of online reviews and surrounding processes. The root of these concerns lies in the number of reviews having no informational value. With the aim of improving the sustainability of this market, the present research develops and compares seven machine learning approaches to identify waste in online app reviews. The Random Forest approach shows the best performance with accuracy of 0.94. If such approach were implemented to reduce data waste in 11 app stores, 252,611 kg of CO2, US$ 74,392 and 25,880 person hours could be saved. Having demonstrated its potential for app reviews, the developed approach could be extended to achieve greater savings and improve sustainability across different segments and types of online reviews.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".