# Supervised Learning of the Global Risk Network Activation from Media Event Reports

## TL;DR

This paper develops a method to validate and analyze global risk networks by creating a risk knowledge graph from Wikipedia, comparing it with WEF data, and building an auto-detection tool to filter relevant media events, revealing insights into risk activation and geographical distribution.

## Contribution

It introduces a novel approach combining knowledge graph construction, network comparison, and an auto-detection tool for media event filtering to study global risk networks.

## Key findings

- Over 50% edge overlap between WEF and Wikipedia risk networks
- Auto-detection tool filters 80% irrelevant media events
- Learned keywords improve risk event filtering

## Abstract

The World Economic Forum (WEF) publishes annual reports on global risks which have the high impact on the world's economy. Currently, many researchers analyze the modeling and evolution of risks. However, few studies focus on validation of the global risk networks published by the WEF. In this paper, we first create a risk knowledge graph from the annotated risk events crawled from the Wikipedia. Then, we compare the relational dependencies of risks in the WEF and Wikipedia networks, and find that they share over 50% of their edges. Moreover, the edges unique to each network signify the different perspectives of the experts and the public on global risks. To reduce the cost of manual annotation of events triggering risk activation, we build an auto-detection tool which filters out over 80% media reported events unrelated to the global risks. In the process of filtering, our tool also continuously learns keywords relevant to global risks from the event sentences. Using locations of events extracted from the risk knowledge graph, we find characteristics of geographical distributions of the categories of global risks.