Maximizing Neutrality in News Ordering
arXiv:2305.15790 · doi:10.1145/3580305.3599425
Abstract
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.
arXiv comment14 pages, 13 figures, accepted to KDD '23
References in corpus (12)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
- Automatic Detection of Fake News
- Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
- Equity of Attention: Amortizing Individual Fairness in Rankings
- Fairness in Ranking: A Survey
- Relation Extraction : A Survey
- On Obtaining Stable Rankings
- Diversification in Session-based News Recommender Systems
- Selection Bias in News Coverage: Learning it, Fighting it
- Maximizing Neutrality in News Ordering
- Empowering Investigative Journalism with Graph-based Heterogeneous Data Management