SynthesisPloS one2025
A systematic review of automated hyperpartisan news detection.
Synthesis in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A deep sentiment model combining ALBERT-driven context and EHO-optimized architecture.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 learning models 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.