ArticlePeerJ. Computer science2021
Fake news detection: a survey of evaluation datasets.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An overview of fake news detection: From a new perspective.Fundamental research · 2025Review
- FinFakeBERT: financial fake news detection.Frontiers in artificial intelligence · 2025Article
- Review
- Automatic detection of health misinformation: a systematic review.Journal of ambient intelligence and humanized computing · 2023Article
- Fake news detection: A survey of graph neural network methods.Applied soft computing · 2023Review
- Normalized effect size (NES): a novel feature selection model for Urdu fake news classification.PeerJ. Computer science · 2023Article
- Evaluating the effectiveness of publishers' features in fake news detection on social media.Multimedia tools and applications · 2023Article
- A systematic literature review and existing challenges toward fake news detection models.Social network analysis and mining · 2022Article
- Combating multimodal fake news on social media: methods, datasets, and future perspective.Multimedia systems · 2022Article
- Modeling and Comparing Brain Processes in Message and Earned Source Credibility Evaluation.Frontiers in human neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.
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Registered trials
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