INSPIRE Track 2: Computational Modeling of Grievances and Political Instability through Global Media

这个INSPIRE奖汇集了通常由社会，行为和经济科学（SBE）理事会社会和经济科学部的政治科学计划支持的研究领域;信息和智能系统部门以及计算机和信息科学与工程（CISE）理事会的网络基础设施办公室;以及数学和物理科学理事会（MPS）的数学科学司。尽管不满与政治不稳定之间的关系已经引起决策者和科学家的关注和兴趣超过世纪，但以前的研究仅限于对国家的比较分析和在选定国家内进行的数量有限的社会调查。这些传统方法昂贵、劳动密集且缓慢。传统方法的弱点的一个鲜明例子是所谓的“阿拉伯之春”事件，该事件导致北非和中东爆发大规模抗议活动;导致埃及、突尼斯和利比亚政权被推翻;在叙利亚煽动了残酷而持久的内战;并引发了巴林的严厉镇压。阿拉伯之春让决策者和学者都感到意外，尽管这些事件似乎在很大程度上是由于数十年的专制统治、普遍的腐败和经济停滞造成的不满而发展起来的。这项研究的主要目的是利用最近从推特等社交媒体渠道和从世界各地大量新闻渠道获得的全球个人层面数据，评估是否有可能在真实的时间内衡量微观层面的不满情绪，以预测不稳定情况。它汇集了计算机科学，数学和社会科学的研究人员，以产生理论和经验的进步。全世界数亿人现在正在使用社交媒体进行交流，使这个技术支持的论坛成为政治参与，表达，倡导和动员的主要事实平台。此外，网上新闻报道的广泛提供使人们能够从世界各地的报纸和其他印刷媒体收集内容，并对所感受到的不满进行编码。通过对社交媒体、在线新闻和传统数据库进行三角测量，该项目评估了它们在确定和测量不满情绪以预测政治不稳定方面的相对优势。这项研究的首要目的是协助政策制定者开发改进的方法来识别和预测不稳定和冲突的热点地区。这不仅对研究有重要意义，而且对国家政策也有重要意义，包括对国防、外交和人道主义援助的战略思考，以及制定潜在的干预措施和评估其实施后的有效性。

This INSPIRE award brings together research areas typically supported by the Political Science Program of the Social and Economic Sciences Division of the Social, Behavioral, and Economic Sciences (SBE) Directorate; the Division of Information and Intelligent Systems and Office of Cyberinfrastructure of the Computer and Information Science and Engineering (CISE) Directorate; and the Division of Mathematical Sciences of the Mathematical and Physical Sciences (MPS) Directorate. Although the relationship between grievances and political instability has concerned and fascinated policymakers and scientists for more than a century, prior research has been limited to comparative analysis of countries and a limited number of social surveys conducted within select countries. These traditional methods are expensive, labor intensive, and slow. A stark example of the weakness of traditional approaches are the events of the so-called "Arab Spring" which resulted in the outbreak of mass protests across North Africa and the Middle East; led to the overthrow of regimes in Egypt, Tunisia, and Libya; fomented a brutal and prolonged civil war in Syria; and triggered severe crack-downs in Bahrain. The Arab Spring caught both policymakers and academics by surprise, even though these events appear to have developed in large part out of grievances that built over decades of autocratic rule, widespread corruption and economic stagnation. The main purpose of this research is to exploit the recent availability of worldwide, individual-level data from social media outlets such as Twitter and from the massive availability of worldwide news outlets to assess the possibility of measuring perceptions of grievances at the micro-level in real time for purposes of forecasting instability. It brings together researchers in computer science, mathematics, and the social sciences to generate theoretical and empirical advances. Hundreds of millions of people around the world are now using social media to communicate, making this technology-enabled forum a major de facto platform for political participation, expression, advocacy, and mobilization. In addition, the widespread availability of online news reports now offers the ability to collect content from newspapers and other print media worldwide and code for perceived grievances. By triangulating measures across social media, the news online, and traditional databases, the project evaluates their relative strength in terms of ascertaining and measuring grievances to forecast political instability. The overarching purpose of this research is to assist policymakers in developing improved methods for identifying and anticipating hot zones of instability and conflict. This has important implications for research but also for national policy, in terms of strategic thinking about defense, diplomacy, and humanitarian assistance, as well as in developing potential interventions and assessing their effectiveness once implemented.