Peacefulness is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peacefulness through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peacefulness.
翻译:和平是福祉的一个主要方面,也是摆脱不平等和暴力的途径。因此,它的衡量方法引起了研究人员、决策者和维和人员的注意。在过去几年里,新的数字数据流极大地改变了这一领域的研究。本项研究利用了从称为全球事件、地点和托恩(GDELT)的新数字数据库中提取的信息,通过全球和平指数(GPI)捕捉和平。应用预测机器学习模型,我们证明GDELT的新闻媒体关注可用作每月测量GPI的代用工具。此外,我们利用可解释的AI技术获取驱动预测的最重要变量。这一分析突出了每个国家的概况,并为预测提供了解释,特别是错误和导致这些错误的事件。我们认为,研究人员、决策者和维和人员利用的数据科学工具,如机器学习一样强大,可以有助于最大限度地提高社会效益和尽量减少和平风险。