---
title: "IMDb: tasks, downloads and usage conditions"
description: "IMDb: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/imdb
markdown_url: https://scholar4ai.com/datasets/imdb.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "IMDb: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# IMDb

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/4.md)

Corpora and benchmarks for language understanding, question answering and text classification.

- data category: Text and NLP benchmarks
- 0 papers in the last 7 days
- Names searched for: IMDb

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Text and NLP benchmarks datasets: https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*NLP dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| IMDb (Large Movie Review Dataset) | Binary sentiment classification of long movie reviews. Evaluation: Accuracy; keep the official split and distinguish document-level reviews from sentence sentiment. | [Official source / documentation](https://nlp.stanford.edu/~amaas/data/sentiment/index.html) Download the original aclImdb archive; 25,000 labeled training and 25,000 test reviews, plus unlabeled data. | Use the release README and source-review terms; public benchmark access is not a blanket grant over IMDb review text. |

Guide updated 2026-11-04.

## Key findings

- From Nov 2023 to Oct 2026, 202 archived papers mentioned IMDb in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jan 2025 (12).
- IMDb was mentioned in 1 paper in the last 30 days (2026-10-07 to 2026-11-05), ranking #165 of the 246 datasets mentioned.
- By journal category: AI (1).
- Most of them appeared in Expert Systems with Applications (1).
- From 2017 to 2026 (so far), IMDb was mentioned in 235 papers in the tracked journals, most of them in AI (192), information systems (12) and marketing (9).
- 68 of them were published in 2025, against 6 in 2017.
- Since 2023, the institutions with the most of these papers were Hong Kong Polytechnic University (5), Sun Yat-sen University (5) and Tsinghua University (5).
- The journals publishing most of them were Artificial Intelligence Review (24), IEEE Transactions on Knowledge and Data Engineering (16) and IEEE Transactions on Neural Networks and Learning Systems (15).
- The most-cited of them is “Autoencoders and their applications in machine learning: a survey” (Artificial Intelligence Review, 2024), with 613 citations.
- Papers use IMDb most often with Twitter / X (51), Yelp (38) and CIFAR-10 / CIFAR-100 (37); it has 47 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 73 | 73 | 56 | 202 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 4 | 2 | 1 | 1 | 4 | 4 | 19 | 61 | 54 | 42 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 2 | 0 | 1 | 0 | 0 | 2 | 2 | 3 | 1 | 1 |
| [Marketing](https://scholar4ai.com/categories/marketing.md) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 3 | 2 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 1 |
| [Tourism and Hospitality](https://scholar4ai.com/categories/tourism-and-hospitality.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 |
| [Health Economics and Policy](https://scholar4ai.com/categories/health-economics-and-policy.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| [Accounting](https://scholar4ai.com/categories/accounting.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 6 | 2 | 2 | 2 | 5 | 6 | 22 | 74 | 68 | 48 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 24 |
| 2 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 16 |
| 3 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 15 |
| 4 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 12 |
| 5 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 12 |
| 6 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 12 |
| 7 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 10 |
| 8 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 10 |
| 9 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 9 |
| 10 | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 9 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 5 |
| 2 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | 5 |
| 3 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 5 |
| 4 | [University of Technology Sydney](https://scholar4ai.com/institutions/university-of-technology-sydney.md) | 5 |
| 5 | [Xidian University](https://scholar4ai.com/institutions/xidian-university.md) | 5 |
| 6 | [Chinese University of Hong Kong](https://scholar4ai.com/institutions/chinese-university-of-hong-kong.md) | 4 |
| 7 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 4 |
| 8 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 4 |
| 9 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 3 |
| 10 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 3 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 79 |
| 2 | United States | 28 |
| 3 | Australia | 23 |
| 4 | Spain | 20 |
| 5 | Italy | 17 |
| 6 | United Kingdom | 16 |
| 7 | Hong Kong | 14 |
| 8 | Netherlands | 11 |
| 9 | Canada | 10 |
| 10 | Germany | 10 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 21 |
| 2 | [sentiment analysis](https://scholar4ai.com/keywords/sentiment-analysis.md) | 19 |
| 3 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 18 |
| 4 | graph classification | 13 |
| 5 | text classification | 11 |
| 6 | [interpretability](https://scholar4ai.com/keywords/interpretability.md) | 10 |
| 7 | [BERT](https://scholar4ai.com/keywords/bert.md) | 9 |
| 8 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 9 |
| 9 | natural language processing | 9 |
| 10 | node classification | 8 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 613 | 18.5 | [Autoencoders and their applications in machine learning: a survey](https://doi.org/10.1007/s10462-023-10662-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-02-03 | Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi et al. |
| 2 | 495 | 14.0 | [A comprehensive survey on applications of transformers for deep learning tasks](https://doi.org/10.1016/j.eswa.2023.122666) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2023-11-22 | Saidul Islam, Hanae Elmekki, Ahmed Elsebai et al. |
| 3 | 483 | 15.9 | [Recommender Systems in the Era of Large Language Models (LLMs)](https://doi.org/10.1109/tkde.2024.3392335) | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 2024-04-22 | Zihuai Zhao, Wenqi Fan, Jiatong Li et al. |
| 4 | 342 | 12.1 | [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal](https://doi.org/10.1109/tpami.2024.3417451) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-06-28 | Ke Liang, Lingyuan Meng, Meng Liu et al. |
| 5 | 214 | 7.3 | [Deepfake video detection: challenges and opportunities](https://doi.org/10.1007/s10462-024-10810-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-05-29 | Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna et al. |

## Used together with

Papers use IMDb most often with Twitter / X (51), Yelp (38) and CIFAR-10 / CIFAR-100 (37); it has 47 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use IMDb together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 51 | 2.4 |
| [Yelp](https://scholar4ai.com/datasets/yelp.md) | Dataset | 38 | 21.2 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 37 | 5.4 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 36 | 6.6 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 35 | 3.0 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 29 | 5.8 |
| [Molecule benchmarks (QM9, ZINC, MUTAG)](https://scholar4ai.com/datasets/molecule-benchmarks-qm9-zinc-mutag.md) | Dataset | 28 | 52.8 |
| [AG News / 20 Newsgroups](https://scholar4ai.com/datasets/ag-news-20-newsgroups.md) | Dataset | 25 | 26.0 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | Dataset | 24 | 8.1 |
| [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | Dataset | 24 | 2.6 |
| [Stanford Sentiment Treebank](https://scholar4ai.com/datasets/stanford-sentiment-treebank.md) | Dataset | 21 | 73.7 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 20 | 1.4 |

The whole network: https://scholar4ai.com/code/network.md

## Papers

*Papers mentioning IMDb in the last 30 days, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-28 | [AP(TM): Associative processor based Tsetlin machine](https://doi.org/10.1016/j.eswa.2026.134830) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Harel Srot, Yehuda Rudin, Alexander Fish et al. | Bar-Ilan University |

## Cite this page

Scholar4AI. (2026). IMDb. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/imdb

```bibtex
@misc{scholar4ai-datasets-imdb,
  author = {{Scholar4AI}},
  title = {IMDb},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/imdb}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).