# Twists and Turns in the US-North Korea Dialogue: Key Figure Dynamic Network Analysis using News Articles

## TL;DR

This paper introduces a novel network analysis method to study key figure interactions in U.S.-North Korea relations using news article co-occurrence data, revealing predictive insights into diplomatic shifts.

## Contribution

It develops a dynamic network analysis approach combining co-occurrence data with Bayesian hidden Markov models to identify structural changes linked to diplomatic events.

## Key findings

- Network structure changes predict key diplomatic moments
- Co-occurrence networks reflect key figure interactions
- Method offers new insights into international relations dynamics

## Abstract

In this paper, we present a method for analyzing a dynamic network of key figures in the U.S.-North Korea relations during the first two quarters of 2018. Our method constructs key figure networks from U.S. news articles on North Korean issues by taking co-occurrence of people's names in an article as a domain-relevant social link. We call a group of people that co-occur repeatedly in the same domain (news articles on North Korean issues in our case) "key figures" and their social networks "key figure networks." We analyze block-structure changes of key figure networks in the U.S.-North Korea relations using a Bayesian hidden Markov multilinear tensor model. The results of our analysis show that block structure changes in the key figure network in the U.S.-North Korea relations predict important game-changing moments in the U.S.-North Korea relations in the first two quarters of 2018.