Maximizing Neutrality in News Ordering
| Autor: | Advani, Rishi, Papotti, Paolo, Asudeh, Abolfazl |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23) (2023) 11--24 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3580305.3599425 |
| Popis: | 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. Comment: 14 pages, 13 figures, accepted to KDD '23 |
| Databáze: | arXiv |
| Externí odkaz: |
|