# No Permanent Friends or Enemies: Tracking Relationships between Nations from News

arXiv: https://arxiv.org/abs/1904.08950 · HTML: https://mlsanity.com/paper/1904.08950/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

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

- [Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames](https://mlsanity.com/paper/2104.11030/) (2021)
- [An Ordinal Latent Variable Model of Conflict Intensity](https://mlsanity.com/paper/2210.03971/) (2022)
- [Classifying Dyads for Militarized Conflict Analysis](https://mlsanity.com/paper/2109.12860/) (2021)
- [Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start](https://mlsanity.com/paper/2304.03414/) (2023)
- [Extracting Victim Counts from Text](https://mlsanity.com/paper/2302.12367/) (2023)

## Abstract (arXiv)

Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nations from news articles. We extend existing models by incorporating shallow linguistics information and propose a new automatic evaluation metric that aligns relationship dynamics with manually annotated key events. As understanding international relations requires carefully analyzing complex relationships, we conduct in-person human evaluations with three groups of participants. Overall, humans prefer the outputs of our model and give insightful feedback that suggests future directions for human-centered models. Furthermore, our model reveals interesting regional differences in news coverage. For instance, with respect to US-China relations, Singaporean media focus more on "strengthening" and "purchasing", while US media focus more on "criticizing" and "denouncing".