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

TL;DR

This paper presents an unsupervised neural approach to infer and analyze the evolving relationships between nations from news articles, incorporating linguistic features and new evaluation metrics, revealing regional differences in coverage.

Contribution

Introduces an enhanced neural model with linguistic features and a novel evaluation metric for tracking international relations from news data.

Findings

Humans prefer the model outputs over baselines.

Model captures regional differences in news coverage.

Reveals specific focus areas in US-China relations across media.

Abstract

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".