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The World Economic Forum (WEF) publishes annual reports on global risks whichhave the high impact on the world's economy. Currently, many researchersanalyze the modeling and evolution of risks. However, few studies focus onvalidation of the global risk networks published by the WEF. In this paper, wefirst create a risk knowledge graph from the annotated risk events crawled fromthe Wikipedia. Then, we compare the relational dependencies of risks in the WEFand Wikipedia networks, and find that they share over 50% of their edges.Moreover, the edges unique to each network signify the different perspectivesof the experts and the public on global risks. To reduce the cost of manualannotation of events triggering risk activation, we build an auto-detectiontool which filters out over 80% media reported events unrelated to the globalrisks. In the process of filtering, our tool also continuously learns keywordsrelevant to global risks from the event sentences. Using locations of eventsextracted from the risk knowledge graph, we find characteristics ofgeographical distributions of the categories of global risks.

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