RIDIR: Modernizing Political Event Data for Big Data Social Science Research

该项目创建了一个通用的研究平台，使用西班牙语，阿拉伯语和法语以及英语的文本来研究民间抗议，国际冲突和内乱。 这扩展了可用于编码区域冲突和合作方法的程序，数据和服务的开发，超越了目前仅使用英语的方法，以实现数据丰富的研究，这将推动社会，行为和经济科学核心问题的新方法。该项目包括一个可公开访问的网站，可以提取和报告地球仪各地的冲突事件，并查明其原因和传播情况。该项目的数据和方法有助于在外交政策、内战预防、人权政策以及环境或经济政策等其他因素对这些现象的影响方面做出数据驱动的决策。 该项目创建了大规模的国内和国家间冲突的措施，涵盖多种新闻来源，并在一个开放的框架内采用共同的方法。 使用多个新闻数据源减少了从单个或一小组新闻源编码所固有的偏见，这是过去的一种常见方法。该项目旨在促进跨语言、跨空间和跨时间的更多、更好的数据编码，从而促进对传统上代表性不足的国家、民族和专题的实质性问题的研究。此外，可用性考虑为用户界面生成了新的软件，用于处理本研究中提出的大数据，以及服务器端优化，可以在不同的用户集上扩展大型数据集。 事件数据的规模涵盖多年和大型新闻数据库，将在空间和时间上产生数百万次观测。 研究工具，数据提取和其他用户界面的开发，使相关的研究社区有机会访问，查询和引用这些数据流。 最后，来自新闻报道的机器编码数据在新闻来源、语言、演员、本体以及人类编码的黄金标准记录中进行验证。这些研究和数据有助于了解气候对拉丁美洲、非洲、中东和世界各地时空参照的国内冲突事件的影响。

The project creates a general research platform to study civil protests, international conflict, and civil unrest using texts from Spanish, Arabic, and French, in addition to English. This expands the development of programs, data and services available for coding regional conflict and cooperation methods beyond the current English-only approaches to enable data-rich research that will advance new approaches to core questions in the social, behavioral, and economic sciences. The project includes an openly available website that allows for the extraction and reporting of conflict events across the globe as well as the identification of their causes and diffusion. The project's data and methods help make data-driven decisions about foreign policy, civil war prevention, human rights policies, and the effects of other factors such as environmental or economic policies on these phenomena. The project creates large-scale civil and inter-state conflict measures, covering multiple news sources and with a common methodology in an open framework. Using multiple news data sources reduces the biases inherent in coding from a single or small set of news sources, a common approach in the past. The project aims to facilitate the coding of more, and better, data across languages, space and time, thus facilitating the study of substantive questions in traditionally underrepresented countries, peoples, and topics. Further, usability considerations generate new software for the user interface for dealing with big data like that proposed in this research, as well as server-side optimizations that scale large datasets across a diverse set of users. The scale of the event data, covering multiple years and large-scale news databases, will generate many millions of observations over space and time. Research tools, data extraction, and other user interfaces are developed to allow the relevant research communities to have access to, queries of, and citation streams for these data. Finally, machine-coded data from news reports is validated across news sources, languages, actors, ontologies, and against human-coded gold standard records. The research and data serve as inputs for understanding the effects of climate on spatio-temporally referenced civil conflict events in Latin American, Africa, the Middle East, and worldwide.