Peacefulness is a principal dimension of well-being for all humankind and is the way out of inequity and every single form of violence. Thus, its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed the research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, 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 the SHAP methodology to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions overall, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by Social Good researchers, policy-makers, and peace-builders, with data science tools as powerful as machine learning, could contribute to maximize the societal benefits and minimize the risks to peacefulness.
翻译:和平是全人类福祉的一个主要方面,也是摆脱不平等和各种暴力形式的途径。因此,衡量和平是最近引起研究人员和决策者注意的。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。在目前的研究中,我们利用从全球事件、地点和托恩(GDELT)数字新闻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的新闻媒体关注可以用作每月测量GPI的代用工具。此外,我们利用SHAP方法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,并为总体预测提供了解释,特别是造成这些错误的错误和事件。我们认为,社会友好研究人员、决策者和建设和平者利用的数字数据数据,以及像机器学习一样强大的数据科学工具,可以有助于最大限度地增加社会效益和尽量减少和平风险。