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

这个INSPIRE奖汇集了社会、行为和经济科学局(SBE)的社会和经济科学部的政治学计划、计算机和信息科学与工程局(CEISE)的信息和智能系统和网络基础设施办公室以及数学和自然科学(MPS)局的数学科学部支持的研究领域。尽管一个多世纪以来，不满和政治不稳定之间的关系一直令政策制定者和科学家关注和着迷，但以前的研究仅限于对各国的比较分析和在选定国家内进行的有限数量的社会调查。这些传统的方法成本高、劳动强度大、速度慢。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，它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；在叙利亚煽动一场残酷和旷日持久的内战；并在巴林引发严厉的镇压。阿拉伯之春让政策制定者和学者都大吃一惊，尽管这些事件似乎在很大程度上是由于数十年的独裁统治、普遍的腐败和经济停滞造成的不满。这项研究的主要目的是利用最近从推特等社交媒体获得的全球个人层面的数据，以及从世界各地大量获得的新闻媒体，评估实时衡量微观层面对不满的看法以预测不稳定的可能性。它将计算机科学、数学和社会科学的研究人员聚集在一起，产生理论和经验上的进步。世界各地数以亿计的人现在正在使用社交媒体进行交流，使这个由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上的平台。此外，广泛使用的在线新闻报道现在提供了从世界各地的报纸和其他印刷媒体收集内容并对感知到的不满进行编码的能力。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衡量民众不满程度以预测政治不稳定方面的相对实力。这项研究的主要目的是协助政策制定者制定改进的方法，以查明和预测不稳定和冲突的热点地区。这不仅对研究有重要影响，而且在国防、外交和人道主义援助方面的战略思考方面，以及在制定潜在干预措施和评估其一旦实施后的有效性方面，也对国家政策具有重要影响。

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.