# Selection Bias in News Coverage: Learning it, Fighting it

arXiv: https://arxiv.org/abs/1904.07536 · HTML: https://mlsanity.com/paper/1904.07536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Computational Assessment of Hyperpartisanship in News Titles](https://mlsanity.com/paper/2301.06270/) (2023)
- [Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines](https://mlsanity.com/paper/2303.15708/) (2023)
- [Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias](https://mlsanity.com/paper/2505.01754/) (2025)

## Abstract (arXiv)

News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events.