emineyetm/fake-news-detection-datasets: papers linking to it

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

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to emineyetm/fake-news-detection-datasets (https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets), 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#

4 papers in the tracked journals link to the Kaggle dataset emineyetm/fake-news-detection-datasets in their Crossref records, the first in 2025; most of them (4) in 2025.
By category: AI (4); the journals with the most are Knowledge and Information Systems (2), Expert Systems with Applications (1) and Cognitive Computation (1).

By year#

20252025

Papers linking to emineyetm/fake-news-detection-datasets by publication year
Year	Papers
2025	4

Journals#

Journals whose papers link to emineyetm/fake-news-detection-datasets most
Journal	Papers
Knowledge and Information Systems	2
Expert Systems with Applications	1
Cognitive Computation	1

Papers linking to emineyetm/fake-news-detection-datasets, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-04-15	AEC: A novel adaptive ensemble classifier with LIME and SHAP-Based interpretability for fake news detection	Expert Systems with Applications	Ashima Kukkar, Gagandeep Kaur	13	cites it
2025-09-25	Advancing fake news combating using machine learning: a hybrid model approach	Knowledge and Information Systems	Zahid Aslam, Malik Muhammad Saad Missen, Arslan Abdul Ghaffar et al.	13	cites it
2025-05-19	Peeping into the Future: Understanding and Combating Generative AI-Based Fake News	Cognitive Computation	Sanjeev Kumar, Siva Sai, Vinay Chamola et al.	5	cites it
2025-08-22	EnsembleNet: a novel deep ensemble learning model with GANBERT and BiLSTM for automated fake news detection	Knowledge and Information Systems	B. M. Brinda, C. Rajan, K. Geetha	4	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). emineyetm/fake-news-detection-datasets: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-emineyetm-fake-news-detection-datasets

@misc{scholar4ai-code-kaggle-emineyetm-fake-news-detection-datasets,
  author = {{Scholar4AI}},
  title = {emineyetm/fake-news-detection-datasets: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-emineyetm-fake-news-detection-datasets}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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