# In Plain Sight: Media Bias Through the Lens of Factual Reporting

arXiv: https://arxiv.org/abs/1909.02670 · HTML: https://mlsanity.com/paper/1909.02670/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 68)

- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias](https://mlsanity.com/paper/2204.04902/) (2022)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics](https://mlsanity.com/paper/2105.11910/) (2021)
- [Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models](https://mlsanity.com/paper/2104.07505/) (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)
- [The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection](https://mlsanity.com/paper/2411.11081/) (2024)
- [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)
- [On Unifying Misinformation Detection](https://mlsanity.com/paper/2104.05243/) (2021)
- [Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification](https://mlsanity.com/paper/2407.17688/) (2024)
- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [Mitigating Media Bias through Neutral Article Generation](https://mlsanity.com/paper/2104.00336/) (2021)
- [The FIGNEWS Shared Task on News Media Narratives](https://mlsanity.com/paper/2407.18147/) (2024)
- [An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles](https://mlsanity.com/paper/2112.13352/) (2021)
- [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)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [Modeling Multi-level Context for Informational Bias Detection by Contrastive Learning and Sentential Graph Network](https://mlsanity.com/paper/2201.10376/) (2022)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)
- [EMONA: Event-level Moral Opinions in News Articles](https://mlsanity.com/paper/2404.01715/) (2024)
- [PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder](https://mlsanity.com/paper/2505.24646/) (2025)
- [Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://mlsanity.com/paper/2506.00068/) (2025)
- ["A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction](https://mlsanity.com/paper/2310.07155/) (2023)

## Abstract (arXiv)

The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection. While prior work in NLP has primarily focused on the lexical bias captured by linguistic attributes such as word choice and syntax, other types of bias stem from the actual content selected for inclusion in the text. In this work, we investigate the effects of informational bias: factual content that can nevertheless be deployed to sway reader opinion. We first produce a new dataset, BASIL, of 300 news articles annotated with 1,727 bias spans and find evidence that informational bias appears in news articles more frequently than lexical bias. We further study our annotations to observe how informational bias surfaces in news articles by different media outlets. Lastly, a baseline model for informational bias prediction is presented by fine-tuning BERT on our labeled data, indicating the challenges of the task and future directions.