Same App, Different App Stores: A Comparative Study
Bibliographic record
Abstract
To attract more users, implementing the same mobile app for different platforms has become a common industry practice. App stores provide a unique channel for users to share feedback on the acquired apps through ratings and textual reviews. However, each mobile platform has its own online store for distributing apps to users. To understand the characteristics of and discrepancies in how users perceive the same app implemented for and distributed through different platforms, we present a large-scale comparative study of cross-platform apps. We mine the characteristics of 80,000 app-pairs (160K apps in total) from a corpus of 2.4 million apps collected from the Apple and Google Play app stores. We quantitatively compare their app-store attributes, such as stars, versions, and prices. We measure the aggregated user-perceived ratings and find many discrepancies across the platforms. Further, we employ machine learning to classify 1.7 million textual user reviews obtained from 2,000 of the mined app-pairs. We analyze discrepancies and root causes of user complaints to understand cross-platform development challenges that impact cross-platform user-perceived ratings. We also follow up with the developers to understand the reasons behind identified discrepancies.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".