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
UI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues of mobile apps. In particular, we analyze more than 3M UI-related reviews from 22,199 top free-to-download apps and 9,380 top non-free apps in the Google Play Store. By comparing the rating of UI-related reviews and other reviews of an app, we observe that UI-related reviews have lower ratings than other reviews. By manually analyzing a random sample of 1,447 UI-related reviews with a 95% confidence level and a 5% interval, we identify 17 UI-related issues types that belong to four categories (i.e., “Appearance,” “Interaction,” “Experience,” and “Others” ). In these issue types, we find “Generic Review” is the most occurring one. “Comparative Review” and “Advertisement” are the most negative two UI issue types. Faced with these UI issues, we explore the patterns of interaction between app owners and users. We identify eight patterns of how app owners dialogue with users about UI issues by the review-response mechanism. We find “Apology or Appreciation” and “Information Request” are the most two frequent patterns. We find updating UI timely according to feedback is essential to satisfy users. Besides, app owners could also fix UI issues without updating UI, especially for issue types belonging to “Interaction” category. Our findings show that there exists a positive impact if app owners could actively interact with users to improve UI quality and boost users’ satisfactoriness about the UIs.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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