# Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.K

arXiv: https://arxiv.org/abs/2006.01211 · HTML: https://mlsanity.com/paper/2006.01211/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 2 of 2)

- [Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models](https://mlsanity.com/paper/2410.18122/) (2024)
- [Tell Me Who Your Friends Are: Using Content Sharing Behavior for News Source Veracity Detection](https://mlsanity.com/paper/2101.10973/) (2021)

## Abstract (arXiv)

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.