Fake News Detection: It’s All in the Data!
Source abstract
Abstract
This brief survey acts as a fundamental resource for researchers beginning their exploration into fake news detection. It emphasizes the importance of dataset quality and diversity in enhancing the effectiveness of detection models, detailing key features, labeling systems, and prevalent biases. It also presents the challenges and limitations. By addressing ethical considerations (such as privacy and consent, societal impacts, transparency, and accountability) and best practices (annotation methodologies, real-world dynamics, reliability, and validity), we offer a thorough overview of current datasets. Additionally, our contribution includes a GitHub repository that aggregates publicly available datasets into a single, easily accessible portal, thereby supporting further research and development in the fight against fake news.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Spam and Phishing Detection · Big Data and Digital Economy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warsaw, Poland
Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Department of Computer Science and Engineering, National Institute of Technology, Zone P1, Plot No. FA7, GT Karnal Rd., Delhi 110036, India
Funding
Horizon Europe grant OMINO · Polish Ministry of Education and Science
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