RT Journal Article
T1 A systematic review of automated hyperpartisan news detection
A1 Maggini, Michele Joshua
A1 Bassi, Davide
A1 Piot, Paloma
A1 Dias, Gaël
A1 Gamallo Otero, Pablo
K1 Deep learning
K1 Machine learning algorithms
K1 Neural networks
K1 Machine learning
K1 Hyperpartisan news detection
AB Hyperpartisan news consists of articles with strong biases that support specific political parties. The spread of such news increases polarization among readers, which threatens social unity and democratic stability. Automated tools can help identify hyperpartisan news in the daily flood of articles, offering a way to tackle these problems. With recent advances in machine learning and deep learning, there are now more methods available to address this issue. This literature review collects and organizes the different methods used in previous studies on hyperpartisan news detection. Using the PRISMA methodology, we reviewed and systematized approaches and datasets from 81 articles published from January 2015 to 2024. Our analysis includes several steps: differentiating hyperpartisan news detection from similar tasks, identifying text sources, labeling methods, and evaluating models. We found some key gaps: there is no clear definition of hyperpartisanship in Computer Science, and most datasets are in English, highlighting the need for more datasets in minority languages. Moreover, the tendency is that deep learningmodels perform better than traditional machine learning, but Large Language Models’ (LLMs) capacities in this domain have been limitedly studied. This paper is the first to systematically review hyperpartisan news detection, laying a solid groundwork for future research.
PB Public Library of Science
SN 1932-6203
YR 2025
FD 2025-02-18
LK https://hdl.handle.net/10347/42212
UL https://hdl.handle.net/10347/42212
LA eng
NO Maggini, M. J., Bassi, D., Piot, P., Dias, G., Gamallo, P. (2025). A systematic review of automated hyperpartisan news detection. PLoS ONE, vol. 20, n. 2
NO This project has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. Funded by the European Union. This work has received financial support from the Xunta de Galicia - Consellería de Cultura, Educación, Formación Profesional e Universidades (Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04 the European Union (European Regional Development Fund - ERDF).
DS Minerva
RD 24 abr 2026