Improving the Efficiency of Militarized Interstate Dispute Data Collection using Automated Textual Analysis

使用自动文本分析提高军事化州际争端数据收集的效率

• 批准号: 0719634
• 负责人: Glenn Palmer
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-15 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0719634&HistoricalAwards=false
• 关键词: Improving; Efficiency; Militarized; Interstate; Dispute

The Militarized Interstate Disputes (MID) dataset is the most prominent and frequently used collection in the study of international conflict. Updating this dataset has become both labor-intensive and prohibitively expensive, with the result being a cycle of delayed updates and escalating costs when updates do occur. To maintain the data on a continuing basis requires a smoother, smaller, more efficient, and less expensive methodology. This project proposes to use automated text analysis algorithms to identify news stories that contain codable militarized interstate actions, which are the most costly and labor-intensive part of the process. The goal is to produce a search process that closely emulates previous human data collection, allowing for more timely and regular updates of the MID dataset in the future. This project will modify a search-engine - TABARI (Textual Analysis By Augmented Replacement Instructions) - that was developed as part of the Kansas Event Data Project (KEDS) to identify news reports that contain information about events of interest to the MID project. The project will do this by experimenting on news reports that have already been coded for the year 2001. We want to refine TABARI so that it can find the news stories that human coders previously identified without producing large numbers of irrelevant reports. Human coders will read the news stories identified by TABARI and verify that they have relevant material or determine that the events reported do not constitute militarized incidents. We expect to refine search procedures so that the new routine can be applied to the task of gathering information in the next broad MID data collection and in subsequent expansions.The broader significance of this project is that successful completion will provide the basis for an update of the MID dataset for the period 2002 through 2008 and nearly continuous updates thereafter. Because the MID data is so widely used by the scholarly community, frequent updates will benefit researchers addressing a wide range of research questions, including but not limited to the democratic peace, arms races, alliances, conflict management, diversionary uses of force, political geography and territorial disputes, crisis escalation, regional security, trade and interdependence, international norms, collective security, military strategy, Palestinian attitudes, intervention, and early warning systems. In particular, expansion of the data set to this new period will allow for stronger examination of issues regarding international conflict in the post-Cold War and post-9/11 world. Moreover, this project will illustrate the feasibility of this type of automated coding for other datasets in the social sciences that are used widely by scholars.

军事化的州际纠纷（中）数据集是在国际冲突研究中最突出，最常用的集合。 更新此数据集既既劳动密集型又过高，因此，当发生更新时，更新的循环和成本不断升级。 为了持续维护数据，需要一个更平稳，更小，更高效且较便宜的方法。 该项目建议使用自动文本分析算法来识别包含军事化的州际行动的新闻报道，这是该过程中最昂贵，最富有劳动力的部分。 目的是产生一个搜索过程，该过程密切模拟了以前的人类数据收集，从而使MID DATASET的及时更新更新。 该项目将修改搜索引擎-TABARI（通过增强替代说明进行文本分析） - 作为堪萨斯州事件数据项目（KEDS）的一部分开发，以识别包含有关MID项目感兴趣事件的信息的新闻报道。 该项目将通过在2001年编码的新闻报告中进行实验。我们希望完善Tabari，以便可以找到人类编码者先前在不产生大量无关报告的情况下找到的新闻报道。 人类编码人员将阅读塔巴里（Tabari）确定的新闻报道，并验证其具有相关材料或确定所报告的事件不是构成军事事件。 我们期望完善搜索程序，以便将新的例程应用于下一个广泛的数据收集和随后的扩展中收集信息的任务。该项目的更广泛的重要性是，成功完成将为更新提供基础2002年至2008年期间的中部数据集，此后几乎连续更新。 由于学术界如此广泛地使用了MID数据，因此频繁的更新将使研究人员受益于解决广泛的研究问题，包括但不限于民主和平，军备竞赛，联盟，冲突管理，武力的转移使用，政治地理以及领土争端，危机升级，地区安全，贸易和相互依存，国际规范，集体安全，军事战略，巴勒斯坦态度，干预和预警系统。 特别是，将数据集的扩展到这一新时期将允许对冷战后战争和9/11世界中国际冲突的问题进行更深入的研究。 此外，该项目将说明社会科学中其他数据集的这种自动编码的可行性，这些编码被学者广泛使用。