---
title: "jruvika/fake-news-detection: papers citing this dataset"
description: "3 papers in the tracked journals link to the Kaggle dataset jruvika/fake-news-detection in their Crossref records, the first in 2020; most of them (1) in 2024."
url: https://scholar4ai.com/code/kaggle-jruvika-fake-news-detection
markdown_url: https://scholar4ai.com/code/kaggle-jruvika-fake-news-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the Kaggle dataset jruvika/fake-news-detection in their Crossref records, the first in 2020; most of them (1) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# jruvika/fake-news-detection: papers linking to it

Research papers in the 480 tracked journals that link to jruvika/fake-news-detection (https://www.kaggle.com/datasets/jruvika/fake-news-detection), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/datasets/jruvika/fake-news-detection

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to the Kaggle dataset jruvika/fake-news-detection in their Crossref records, the first in 2020; most of them (1) in 2024.
- By category: AI (3); the journals with the most are Expert Systems with Applications (2) and Journal Of Big Data (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links jruvika/fake-news-detection.

## By year

*Papers linking to jruvika/fake-news-detection by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2021 | 1 |
| 2024 | 1 |

## Journals

*Journals whose papers link to jruvika/fake-news-detection most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles](https://doi.org/10.1016/j.eswa.2021.115002) (Expert Systems with Applications, 2021)
  - [An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection](https://doi.org/10.1186/s40537-024-00895-9) (Journal Of Big Data, 2024) — cites the paper above

## Papers

*Papers linking to jruvika/fake-news-detection, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-05-25 | [Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms](https://doi.org/10.1016/j.eswa.2020.113584) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Yin‐Fu Huang, Po-Hong Chen | 151 | cites it |
| 2021-04-07 | [A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles](https://doi.org/10.1016/j.eswa.2021.115002) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Priyanka Meel, Dinesh Kumar Vishwakarma | 81 | cites it |
| 2024-03-19 | [An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection](https://doi.org/10.1186/s40537-024-00895-9) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Amr A. Abd El-Mageed, Amr A. Abohany, Asmaa H. Ali et al. | 13 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). jruvika/fake-news-detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-jruvika-fake-news-detection

```bibtex
@misc{scholar4ai-code-kaggle-jruvika-fake-news-detection,
  author = {{Scholar4AI}},
  title = {jruvika/fake-news-detection: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-jruvika-fake-news-detection}},
  urldate = {2026-11-03},
  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/).