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

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Abstract

The World Economic Forum (WEF) publishes annual reports of 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.

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Acknowledgements

This work was supported in part by the Army Research Office (ARO) under Grant W911NF-16-1-0524, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (NS CTA), and by the Office of Naval Research (ONR) under Grant N00014-15-1-2640. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies either expressed or implied of the Army Research Laboratory or the U.S. Government.

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Authors and Affiliations

  1. Department of Computer Science, Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, USA

    Xiang Niu & Boleslaw K. Szymanski

  2. Department of Physics, Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY, USA

    Gyorgy Korniss

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  1. Xiang Niu
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  2. Gyorgy Korniss
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Correspondence to Boleslaw K. Szymanski.

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This article is part of the topical collection “Modelling methods in Computer Systems, Networks and Bioinformatics” guest edited by Erol Gelenbe.

Appendix

Appendix

Tables 4, 5, 6, 7, 8 contain subordinated tags for each risk within one of the five categories of risks.

Table 4 Economic risks and subordinate tags
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Table 5 Environmental risks and their corresponding tags
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Table 6 Geopolitical risks and their corresponding tags
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Table 7 Societal risks and their corresponding tags
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Table 8 Technological risks and their corresponding tags
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Niu, X., Korniss, G. & Szymanski, B.K. Supervised Learning of the Global Risk Network Activation from Media Event Reports. SN COMPUT. SCI. 1, 29 (2020). https://doi.org/10.1007/s42979-019-0036-6

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