# Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

arXiv: https://arxiv.org/abs/2010.10652 · HTML: https://mlsanity.com/paper/2010.10652/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [On the Independence of Association Bias and Empirical Fairness in Language Models](https://mlsanity.com/paper/2304.10153/) (2023)
- [Do You Think It's Biased? How To Ask For The Perception Of Media Bias](https://mlsanity.com/paper/2112.07392/) (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)
- [Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles](https://mlsanity.com/paper/2211.03491/) (2022)
- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [Steering Towards Fairness: Mitigating Political Bias in LLMs](https://mlsanity.com/paper/2508.08846/) (2025)
- [An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles](https://mlsanity.com/paper/2112.13352/) (2021)
- [Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy](https://mlsanity.com/paper/2304.02247/) (2023)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media](https://mlsanity.com/paper/2211.03634/) (2022)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)
- [Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://mlsanity.com/paper/2506.00068/) (2025)
- [Controlled Neural Sentence-Level Reframing of News Articles](https://mlsanity.com/paper/2109.04957/) (2021)
- [Pitfalls of Conversational LLMs on News Debiasing](https://mlsanity.com/paper/2404.06488/) (2024)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings](https://mlsanity.com/paper/2104.07814/) (2021)
- [Data-Augmented and Retrieval-Augmented Context Enrichment in Chinese Media Bias Detection](https://mlsanity.com/paper/2311.01372/) (2023)
- [Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection](https://mlsanity.com/paper/2405.18974/) (2024)
- [A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches](https://mlsanity.com/paper/2109.09014/) (2021)
- [Capturing Topic Framing via Masked Language Modeling](https://mlsanity.com/paper/2302.03183/) (2023)
- [On Context-aware Detection of Cherry-picking in News Reporting](https://mlsanity.com/paper/2401.05650/) (2024)
- [Journalism-Guided Agentic In-Context Learning for News Stance Detection](https://mlsanity.com/paper/2507.11049/) (2025)
- [Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach](https://mlsanity.com/paper/2106.14387/) (2021)
- [Voting Booklet Bias: Stance Detection in Swiss Federal Communication](https://mlsanity.com/paper/2306.08999/) (2023)
- [KUCST at CheckThat 2023: How good can we be with a generic model?](https://mlsanity.com/paper/2306.09108/) (2023)
- [Multimodal Political Bias Identification and Neutralization](https://mlsanity.com/paper/2506.17372/) (2025)
- [Political Leaning and Politicalness Classification of Texts](https://mlsanity.com/paper/2507.13913/) (2025)

## Abstract (arXiv)

Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed. The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. In this regard we utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com and develop a neural model for bias assessment. By analyzing this model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.