Pith. sign in

REVIEW 3 major objections 6 minor 19 references

GLARE: Google Apps Arabic Reviews Dataset

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read GLARE releases 69 million Arabic app reviews, the largest Arabic corpus of its kind.

desk verdict Useful dataset at heart, but Table 3 contradicts the 69M Arabic review headline, leaving the central claim unverified. read the letter →

arxiv 2412.15259 v1 pith:B5OWMAIL submitted 2024-12-16 cs.CL cs.SI

Fatima AlGhamdi , Reem Mohammed , Hend Al-Khalifa , Areeb Alowisheq This is my paper · ORCID
classification cs.CLcs.SI
keywords ArabicNLPappreviewsGooglePlayStoresentimentanalysisaspectbasedfeatureengineeringexploratorydatalow-resourcelanguages
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

GLARE is a new Arabic-language resource: 76,512,077 reviews harvested from the Saudi Google Play Store, of which the authors count 69 million as Arabic, across 9,980 unique Android applications. The paper's aim is to give Arabic NLP a corpus of app-store reviews at a scale that earlier Arabic review datasets do not approach, with the closest comparable dataset containing about 51,000 reviews. The corpus includes ratings, thumbs-up counts, developer replies, and app metadata, and the paper reports exploratory statistics and engineered features meant to support sentiment analysis, aspect-based sentiment analysis, and software-engineering analysis of user feedback. If the size claim holds, GLARE would supply the Arabic review domain with enough text for deep-learning-scale models rather than the small annotated sets that have dominated the area.

What carries the argument

The load-bearing mechanism is the collection and filtering pipeline behind the corpus: a category-driven crawl of the Saudi Google Play Store that targets the top 200 free apps in each of 59 main and sub categories, maps apps to their category sets, drops duplicates to reach 9,980 unique applications, and scrapes reviews together with ratings, thumbs-up counts, developer replies, and app metadata. The step that turns the raw 76.5M reviews into the headline 69M Arabic figure is a preprocessing filter described only as 'keeping only Arabic reviews'; the paper does not specify how Arabic is detected, and that filter is what the size claim rests on. The feature-engineering pass builds a term-frequency dictionary with the token-counting utility CountVectorizer, producing 8.7M unique tokens and statistics on word length, review length, and noise that quantify how much cleaning downstream users will need.

What would settle it

Download a random sample of the raw 76M reviews, label it with a transparent Arabic-script and dialect criterion, estimate the Arabic proportion from the sample, and compare it with the 69M/76M ratio; a material mismatch would show the headline count is an artifact of the unspecified filter.

Watch

Extended reading notes

Core claim

The claim on the paper's own terms is that GLARE is the largest Arabic reviews dataset to date. The crawl collected 76,512,077 reviews between March 21 and April 21 from the Saudi Google Play Store, starting from the top 200 free apps in each of 59 categories; after removing duplicated apps, the corpus covers 9,980 unique apps and 17 GB of raw review text. A preprocessing step that drops duplicates, nulls, symbols, numbers, and 'noise' and keeps only Arabic reviews yields the headline 69M Arabic reviews. The paper does not train or evaluate a model; its contribution is the resource itself plus descriptive analysis of ratings, thumbs-up votes, developer replies, vocabulary, review length, and app categories.

Load-bearing premise

The 69 million Arabic count rests on an undocumented language-filtering step, so the reported Arabic share depends on a filter the reader cannot inspect or reproduce.

Editorial extensions

If this is right

  • Arabic sentiment analysis and aspect-based sentiment analysis can be trained on tens of millions of in-domain reviews rather than thousands.
  • The rating attached to each review gives a free distant-supervision signal for opinion mining.
  • App-store reviews provide a text genre distinct from the Twitter posts that dominate most existing Arabic corpora, with a higher character ceiling and reply metadata.
  • Software engineering researchers and practitioners can use the corpus to study feature requests, bugs, rating dynamics, and developer-response behavior.
  • The reported vocabulary and noise statistics imply that downstream users will need substantial cleaning, especially because over 40% of reviews are a single word.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Until the Arabic-detection filter is documented, the 69M figure should be read as an order-of-magnitude estimate rather than an exact measurement.
  • Because the storefront is the Saudi Google Play, the Arabic text likely skews toward Saudi-region vocabulary and Modern Standard Arabic, making GLARE a geography-specific sample rather than a balanced pan-Arab corpus.
  • The authors list a domain-specific Arabic language model and an aspect-based sentiment analysis benchmark as future work, so the dataset's utility for those tasks remains untested in this paper.
  • If the one-word reviews are mostly generic praise, task-specific filtering could shrink the practically usable corpus well below 69M, so the effective size for NLP may differ from the headline size.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces GLARE, a corpus of Google Play app reviews collected from the Saudi Google Play Store in March-April 2021. The authors scraped reviews for 9,980 unique apps across 59 categories, producing about 76M review records and 17 GB of raw data. They claim that after cleaning and 'keeping only Arabic reviews' the corpus contains over 69M Arabic reviews, and they present exploratory statistics (ratings, thumbs-up counts, developer replies), feature engineering (vocabulary, review length, duplicate apps, category combinations), and potential use cases for Arabic NLP. The dataset is released via GitHub and Hugging Face.

Significance. If the 69M Arabic subset is real and reproducible, GLARE would be a substantial new resource for Arabic NLP, particularly for app-review sentiment analysis and software-engineering tasks, and it would be larger than the Arabic review datasets cited in the related work. The paper's strengths are the clear collection outline, the useful metadata fields, and the public release of raw and engineered files. The descriptive statistics in Sections 4 and 5 are internally consistent with the raw 76M corpus. However, the central 'largest Arabic reviews dataset' claim is not yet established: the paper does not document how Arabic reviews were identified, and the feature-engineering table is based on the unfiltered corpus rather than the claimed 69M subset. These are load-bearing but correctable issues.

major comments (3)
  1. [§3.1 and §5 / Table 3] The 69M Arabic-review count is contradicted by the paper's own statistics. Table 3 reports Total Number of Reviews = 76,512,077 and Total Non-empty Reviews = 76,387,928, and the one-word percentage (40.5%) is computed from 30,953,303 / 76,387,928 = 40.52%. Using the claimed 69M Arabic subset would give about 44.8%. Section 5.1 explicitly states that the vocabulary is 'extracted from all the reviews in GLARE dataset', and Section 5.2 describes tokenizing 'the reviews' without limiting to the Arabic subset. Therefore the EDA and feature engineering were performed on the full 76M corpus, and the abstract/conclusion claim that 69M Arabic reviews were isolated is not corroborated by any table or analysis. The authors should either release and analyze the Arabic-filtered subset separately and report its statistics, or revise the central claim to describe a raw 76M corpus with an Arabic subset whose size is verified by a documented filter.
  2. [§3.1] The Arabic language filter is not described. 'Keeping only Arabic reviews' could mean script-based filtering, a language identification model, a lexicon, or manual rules, and each choice materially changes the 69M count and the vocabulary statistics. The paper should name the tool/library/model (with version and language code), the threshold or confidence score, and how mixed-script, transliterated, and dialectal Arabic reviews were handled. Without this, the headline number is not reproducible.
  3. [§1 and §2] The 'largest Arabic reviews dataset to date' claim is not benchmarked against the full range of existing Arabic review corpora. The related-work section lists several small review datasets, but it does not survey the Masader catalogue or other large review resources to justify the superlative. Because the comparison corpus is not enumerated and the size of the Arabic subset is itself in question, this claim needs a concrete comparison table or an explicit scope, such as 'largest Arabic app-review dataset'.
minor comments (6)
  1. [§8] The license is described as 'Creative Commons Attribution-Non-Commercial 4.0 International License (CC BY 4.0)', but CC BY 4.0 is not a non-commercial license; please correct the license name and URL to match the actual license.
  2. [§3.1 / Table 2] The collection period 'March 21 - April 21' should include the year, e.g., March 21, 2021 – April 21, 2021, along with the exact scraper and library versions used.
  3. [§5.1] The definition of 'noise' is ambiguous: it is first reported as 17% of the vocabulary, then 15% of vocabulary words 'had digits', and later defined as 'any character that is non-Arabic alphanumeric'. Please define these categories as mutually exclusive or explain the overlap.
  4. [§5.1] The top frequent terms are given as glyph-name sequences rather than in Arabic script; rendering the actual Arabic words would improve readability.
  5. [§3.1 / §5.3] The paper says 'over 11K apps' in Section 3.1 and '11,736 apps' in Section 5.3; please harmonize the two numbers.
  6. [§3.1 / §7] No scraping or cleaning code is provided in the repository; for a dataset paper, adding the collection and cleanup scripts, or at least a detailed pseudocode, would support reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: GLARE is a descriptive dataset paper with no derived prediction or fitted parameter to reduce.

full rationale

The paper reports the construction and descriptive statistics of a newly collected Arabic app-reviews dataset. There is no derivation chain in which an output quantity is defined in terms of an input quantity, no fitted parameter is relabeled as a prediction, and no load-bearing argument rests on a self-citation. The central claim, that GLARE contains 76M reviews, 69M of which are Arabic, is a reported collection outcome rather than a tested hypothesis. The only substantive concern is internal consistency: Section 3.1 says the dataset was filtered to keep only Arabic reviews, while Table 3 reports statistics over 76,387,928 non-empty reviews, and the one-word-review percentage of 40.5% is consistent with the 76M denominator rather than the claimed 69M Arabic subset. This is a correctness or reproducibility issue about whether the language filter was actually applied and how Arabic was detected, not a circularity issue: no quantity is being predicted from, or defined in terms of, another quantity. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

GLARE is a dataset, not a theory. The central claims rest on scraping source assumptions, the Arabic language filter, and the app selection strategy. There are no fitted parameters and no invented entities.

assumptions (3)
  • domain assumption google-play-scraper returns the complete set of accessible reviews and correct app metadata for the Saudi Google Play Store.
    Section 3.1 states that reviews were scraped with google-play-scraper, but the paper does not validate against Play Store API limits, pagination behavior, or store-side filtering.
  • domain assumption The rule used to keep only Arabic reviews is correct, even though it is not specified.
    Section 3.1 lists 'keeping only Arabic reviews' as a preprocessing step, but no language identification tool, lexicon, or heuristic is given. The 69M Arabic count depends on this step.
  • domain assumption Top 200 free apps per category adequately represents the Saudi Google Play Store.
    Section 3.1 says the authors scraped the top 200 free apps from each category. This selection choice is not justified and affects which apps and reviews are covered.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GLARE: Google Apps Arabic Reviews Dataset." pith.science (2026). https://pith.science/paper/B5OWMAIL

@misc{pith2026241215259,
  author       = {Pith},
  title        = {Pith review of: GLARE: Google Apps Arabic Reviews Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B5OWMAIL}},
  note         = {Machine review of arXiv:2412.15259}
}
read the original abstract

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.

Figures

Figures reproduced from arXiv: 2412.15259 by the authors.

Figure 1
Figure 1. Apps Metadata [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Reviews Metadata that ranges from 1 to 5. Analysing ratings of the reviews and their effect on various properties such as inciting developers to reply or how user ratings change overtime can be of help in software maintenance and evolution life-cycle (D ˛abrowski et al., 2022). In our dataset, we found that the ratings are skewed greatly with over 80% of the reviews having 5 stars. Additional statistics of ratings d… view at source ↗
Figure 4
Figure 4. Percentage of Developers Engagement with re [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Statistics of Thumbs-up with respect to Ratings [PITH_FULL_IMAGE:figures/full_fig_p003_3.png]
Figure 5
Figure 5. Figure 5: Top 80% Most Frequent Characters Length per Word with respect to the Total Number of Words in the Vocabulary [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Top 200 Most Frequent Words. length of 6 characters as shown in [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Samples from the Top Longest Reviews. gories. The application ID was used to scrape reviews, and duplicated apps were only scraped once. Duplicated apps were handled by keeping only one instance of the app. The final list of selected applications consists of 9,980 uniq…
Figure 8
Figure 8. Figure 8: Percentage of Reviews in Top 20 Most Reviewed Apps. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 15 canonical work pages

  1. [1]

    Abdul-Mageed, M., Alhuzali, H., and Elaraby, M. (2018). You tweet what you speak: A city-level dataset of arabic dialects. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

    work page 2018

  2. [2]

    A., and Househ, M

    Ahmed, A., Ali, N., Alzubaidi, M., Zaghouani, W., Abd-alrazaq, A. A., and Househ, M. (2022). Freely available arabic corpora: A scoping review. Computer Methods and Programs in Biomedicine , 2:100049

    work page 2022

  3. [3]

    A., Jararweh, Y., and Al-Kabi, M

    Al-Ayyoub, M., Khamaiseh, A. A., Jararweh, Y., and Al-Kabi, M. N. (2019). A comprehensive survey of arabic sentiment analysis. Information processing & management , 56(2):320--342

    work page 2019

  4. [4]

    Al-Shamani, M., Al-Sarem, M., Saeed, F., and Almutairi, W. (2022). Designing an arabic google play store user review dataset for detecting app requirement issues. In Advances on Smart and Soft Computing , pages 133--143. Springer

    work page 2022

  5. [5]

    Al-Smadi, M., Qawasmeh, O., Talafha, B., and Quwaider, M. (2015). Human annotated arabic dataset of book reviews for aspect based sentiment analysis. In 2015 3rd International Conference on Future Internet of Things and Cloud , pages 726--730. IEEE

    work page 2015

  6. [6]

    A., Sarro, F., Black, S., Capra, L., and Harman, M

    Al-Subaihin, A. A., Sarro, F., Black, S., Capra, L., and Harman, M. (2019). App store effects on software engineering practices. IEEE Transactions on Software Engineering , 47(2):300--319

    work page 2019

  7. [7]

    I., and Zhang, X

    Alharbi, B., Alamro, H., Alshehri, M., Khayyat, Z., Kalkatawi, M., Jaber, I. I., and Zhang, X. (2020). Asad: A twitter-based benchmark arabic sentiment analysis dataset. arXiv preprint arXiv:2011.00578

    arXiv 2020

  8. [8]

    S., Mansour, W., Elsayed, T., and Al-Ali, A

    Ali, Z. S., Mansour, W., Elsayed, T., and Al-Ali, A. (2021). Arafacts: the first large arabic dataset of naturally occurring claims. In Proceedings of the Sixth Arabic Natural Language Processing Workshop , pages 231--236

    work page 2021

Show all 19 references
  1. [9]

    Alsarsour, I., Mohamed, E., Suwaileh, R., and Elsayed, T. (2018). Dart: A large dataset of dialectal arabic tweets. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

    2018

  2. [10]

    and Atiya, A

    Aly, M. and Atiya, A. (2013). Labr: A large scale arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages 494--498

    2013

  3. [11]

    Alyafeai, Z., Masoud, M., Ghaleb, M., and Al-shaibani, M. S. (2021). Masader: Metadata sourcing for arabic text and speech data resources. arXiv preprint arXiv:2110.06744

    2021 arXiv

  4. [12]

    D a browski, J., Letier, E., Perini, A., and Susi, A. (2022). Analysing app reviews for software engineering: a systematic literature review. Empirical Software Engineering , 27(2):1--63

    2022

  5. [13]

    Einea, O., Elnagar, A., and Al Debsi, R. (2019). Sanad: Single-label arabic news articles dataset for automatic text categorization. Data in brief , 25:104076

    2019

  6. [14]

    S., and Einea, A

    Elnagar, A., Khalifa, Y. S., and Einea, A. (2018). Hotel arabic-reviews dataset construction for sentiment analysis applications. In Intelligent Natural Language Processing: Trends and Applications , pages 35--52. Springer

    2018

  7. [15]

    Haouari, F., Hasanain, M., Suwaileh, R., and Elsayed, T. (2020). Arcov-19: The first arabic covid-19 twitter dataset with propagation networks. arXiv preprint arXiv:2004.05861

    2020 arXiv

  8. [16]

    and Ghanem, B

    Mulki, H. and Ghanem, B. (2021). Let-mi: An arabic levantine twitter dataset for misogynistic language. arXiv preprint arXiv:2103.10195

    2021 arXiv

  9. [17]

    B., Elnagar, A., Shahin, I., and Henno, S

    Nassif, A. B., Elnagar, A., Shahin, I., and Henno, S. (2021). Deep learning for arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing , 98:106836

    2021

  10. [18]

    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in P ython. Jou...

    2011

  11. [19]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.