Jun Hu, Xueming Shu, & Shiyang Tang. (2018). Analysis of Core Social Actors in Nine Types of Mass Incidents Based on Social Network Analysis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 219–231). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: This article studies on the cases of nine types of mass incidents happened in China during the last decade, and study the relationships among various social actors in different types of mass incidents with the methods of social networks from the perspective of relational theory. By constructing the social network of mass incidents, we statistically analyze the relationship intensity between social actors in different types of mass incidents, and calculate the centrality degrees of social actors, which can be regard as index to characterize the social risk of social actors. Meanwhile, we also analyze the cliques and “core-periphery” structure in the social network of mass incidents to get the core social actors in mass incidents, thus providing decision-making references for social leaders to effectively deal with mass incidents and improving emergency response capabilities.
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Lida Huang, Guoray Cai, Hongyong Yuan, Jianguo Chen, Yan Wang, & Feng Sun. (2018). Modeling Threats of Mass Incidents Using Scenario-based Bayesian Network Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 121–134). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library
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