# Maximizing Neutrality in News Ordering

arXiv: https://arxiv.org/abs/2305.15790 · HTML: https://mlsanity.com/paper/2305.15790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 1 of 1)

- [Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning](https://mlsanity.com/paper/2501.00884/) (2025)

## Abstract (arXiv)

The detection of fake news has received increasing attention over the past few years, but there are more subtle ways of deceiving one's audience. In addition to the content of news stories, their presentation can also be made misleading or biased. In this work, we study the impact of the ordering of news stories on audience perception. We introduce the problems of detecting cherry-picked news orderings and maximizing neutrality in news orderings. We prove hardness results and present several algorithms for approximately solving these problems. Furthermore, we provide extensive experimental results and present evidence of potential cherry-picking in the real world.