A systematic review of automated hyperpartisan news detection.
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...
Saved in:
| Main Authors: | Michele Joshua Maggini, Davide Bassi, Paloma Piot, Gaël Dias, Pablo Gamallo Otero |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316989 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Systematic Literature Review on Machine Learning Algorithms for the Detection of Social Media Fake News in Africa
by: Joshua Ebere Chukwuere, et al.
Published: (2025-06-01) -
Zero-Shot Automated Detection of Fake News: An Innovative Approach (ZS-FND)
by: Rania Baashirah
Published: (2024-01-01) -
An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection
by: Yexin Tian, et al.
Published: (2025-06-01) -
AI-Driven Chatbot for Real-Time News Automation
by: Fahim Sufi, et al.
Published: (2025-03-01) -
Automated detection of hospital outbreaks: A systematic review of methods.
by: Brice Leclère, et al.
Published: (2017-01-01)