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

INSPIRE 轨道 2：通过全球媒体对不满和政治不稳定进行计算建模

• 批准号: 1343123
• 负责人: Gary LaFree
• 金额: $ 259.45万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1343123&HistoricalAwards=false
• 关键词: INSPIRE; Track; Computational; Modeling; Grievances

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.

该 INSPIRE 奖项汇集了通常由社会、行为和经济科学 (SBE) 理事会社会和经济科学部政治学项目支持的研究领域；计算机与信息科学与工程局信息与智能系统处、网络基础设施办公室；以及数学和物理科学 (MPS) 理事会的数学科学部。尽管一个多世纪以来，不满情绪与政治不稳定之间的关系一直令政策制定者和科学家关注并着迷，但先前的研究仅限于对国家的比较分析以及在选定国家内进行的有限数量的社会调查。这些传统方法成本昂贵、劳动强度大且速度慢。传统方法弱点的一个明显例子是所谓的“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；在叙利亚煽动残酷而长期的内战；并引发了巴林的严厉镇压。阿拉伯之春让政策制定者和学者都感到惊讶，尽管这些事件似乎在很大程度上是由于几十年来的独裁统治、普遍的腐败和经济停滞而产生的不满情绪而发展起来的。这项研究的主要目的是利用 Twitter 等社交媒体和全球新闻媒体的大量可用数据来评估在微观层面衡量不满情绪的可能性。实时用于预测不稳定性。它汇集了计算机科学、数学和社会科学领域的研究人员，以取得理论和实证方面的进展。现在，世界各地有数亿人使用社交媒体进行交流，使这个技术支持的论坛成为事实上的政治参与、表达、宣传和动员的主要平台。此外，在线新闻报道的广泛使用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编码感知不满的能力。通过对社交媒体、在线新闻和传统数据库的衡量指标进行三角测量，该项目评估了它们在查明和衡量不满情绪方面的相对优势，以预测政治不稳定。这项研究的首要目的是协助决策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。这不仅对研究具有重要意义，而且对国家政策、国防、外交和人道主义援助的战略思考以及制定潜在干预措施和评估其实施后的有效性也具有重要意义。