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.

军事化的国家间争端(MID)数据集是国际冲突研究中最突出和最频繁使用的数据集。更新此数据集既费力又贵得令人望而却步，其结果是，当更新发生时，更新会出现延迟和成本上升的循环。要持续维护数据，需要一种更流畅、更小、更高效、成本更低的方法。该项目建议使用自动文本分析算法来识别包含可编码的军事化州际行动的新闻故事，这是该过程中成本最高、劳动最密集的部分。其目标是产生一个紧密模仿以前的人类数据收集的搜索过程，允许在未来更及时和定期地更新MID数据集。该项目将修改作为堪萨斯事件数据项目(KEDS)一部分开发的搜索引擎-Tabari(通过扩充替换说明进行文本分析)，以确定包含MID项目感兴趣的事件信息的新闻报道。该项目将通过对已经为2001年编码的新闻报道进行试验来做到这一点。我们希望改进Tabari，这样它就可以找到人类编码员以前识别的新闻故事，而不会产生大量不相关的报告。人类编码员将阅读Tabari确定的新闻故事，并核实他们是否拥有相关材料，或确定所报告的事件不构成军事化事件。我们期望改进搜索程序，以便新的例行程序可以应用于在下一次广泛的中期数据收集和随后的扩展中收集信息的任务。这一项目的更广泛的意义在于，成功完成这一项目将为2002年至2008年期间中期数据集的更新以及此后几乎连续的更新提供基础。由于中期数据被学术界广泛使用，频繁更新将有助于研究人员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、转而使用武力、政治地理和领土争端、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、巴勒斯坦人的态度、干预和早期预警系统。特别是，将数据集扩大到这一新时期，将使我们能够更有力地审查有关冷战后和9/11事件后世界的国际冲突问题。此外，该项目将说明这种类型的自动编码对于学者们广泛使用的社会科学中的其他数据集的可行性。