Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open-source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.

中文翻译：

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使用机器学习方法对 114 183 次恐怖袭击进行自动和客观的分级

灾难性事件造成人员伤亡、财产损失，并导致巨大的社会影响。为了建立共同的标准并促进灾害的国际交流，社会管理部门的相关部门根据直接经济损失和生命损失等主观强加的术语对灾害进行排名。涉及不确定人为因素的恐怖袭击，根据财产损失规则进行粗略分级，更难以解释和评估。在本文中，我们收集了 114 183 条恐怖袭击的开源记录，并使用机器学习方法以自动、客观的方式对其进行综合评分。评分不涉及主观主张或个人喜好，推导出的每一个公因子都包含了众多变量的全面丰富的信息。我们的工作提出了一种新的自动排序方法，适用于广泛的分级问题。此外，我们可以使用此模型对所有此类攻击进行全球评级并将其可视化以提供新的见解。