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

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

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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.

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#

20202024

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	2
Journal Of Big Data	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles Expert Systems with Applications, 2021
- An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection Journal Of Big Data, 2024 — cites the paper above

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	Expert Systems with Applications	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	Expert Systems with Applications	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	Journal Of Big Data	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

@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}
}

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