# We Can Detect Your Bias: Predicting the Political Ideology of News Articles

arXiv: https://arxiv.org/abs/2010.05338 · HTML: https://mlsanity.com/paper/2010.05338/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 30 of 92)

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- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media](https://mlsanity.com/paper/2204.04046/) (2022)
- [Interpretable Propaganda Detection in News Articles](https://mlsanity.com/paper/2108.12802/) (2021)
- [IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators](https://mlsanity.com/paper/2402.00345/) (2024)
- [KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media](https://mlsanity.com/paper/2108.03861/) (2021)
- [Modelling Political Coalition Negotiations Using LLM-based Agents](https://mlsanity.com/paper/2402.11712/) (2024)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness](https://mlsanity.com/paper/2405.02714/) (2024)
- [SoMeLVLM: A Large Vision Language Model for Social Media Processing](https://mlsanity.com/paper/2402.13022/) (2024)
- [Evaluating Decision Models for Text Annotation in Computational Social Science](https://mlsanity.com/paper/2609.24574/) (2026)
- [Generating Media Background Checks for Automated Source Critical Reasoning](https://mlsanity.com/paper/2409.00781/) (2024)
- [Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning](https://mlsanity.com/paper/2506.08354/) (2025)
- [Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper](https://mlsanity.com/paper/2310.16269/) (2023)
- [A Template Is All You Meme](https://mlsanity.com/paper/2311.06649/) (2023)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations](https://mlsanity.com/paper/2302.06452/) (2023)
- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation](https://mlsanity.com/paper/2504.20500/) (2025)
- [Probably Approximately Correct Labels](https://mlsanity.com/paper/2506.10908/) (2025)
- [What Is The Political Content in LLMs' Pre- and Post-Training Data?](https://mlsanity.com/paper/2509.22367/) (2025)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://mlsanity.com/paper/2301.11850/) (2023)
- [Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy](https://mlsanity.com/paper/2304.02247/) (2023)

## Abstract (arXiv)

We explore the task of predicting the leading political ideology or bias of news articles. First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media. We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology. From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss. We further add background information about the source, and we show that it is quite helpful for improving article-level prediction. Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.