Welcome to the project Civil Strife website. Here you will find the most recent data releases from this project.
Project Civil Strife is a collection of dyadic event data sets focusing on political conflict and cooperation processes among myriad actors within countries. The bulk of the project focused on South and Southeast Asian nations from 2001-2010 and all data were machine coded using the Xenophon event data coding engine developed by Strategic Analysis Enterprises. Most of the data are provided at the country level. We have also developed a proprietary multi-method geo-coding software package to geo-locate the events within districts and provinces within countries. At this time we are releasing only some of the India data that were used in our Minerva study. More geo-located data will be available later and/or one can contact Professor Shellman directly for more information or data needs.
This project and these data are a culmination of U.S. National Science Foundation, Defense Advanced Research Projects Agency, Office of Secretary of Defense, Office of Naval Research and Strategic Analysis Enterprises internal research and development funding. These data are being released as deliverables for National Science Foundation projects (BCS-0904921; SES-0721618; SES-0516545; SES-0214287) but the coding engine used to code the data benefitted from several other sources of funding and the data improved overtime as a result. Moreover, the actor and event dictionaries were developed and expanded over time with additional funding. The agencies and project numbers are provided below:
2009-11 National Science Foundation Grant, NSCC/SA: Terror, Conflict Processes, Organizations, & Ideologies: Completing the Picture. DOD-NSF Minerva Initiative (NSF: BCS-0904921).
2007-09 National Science Foundation Grant, SES-0721618. Domestic Terrorism & Political Violence: Empirical Models of Government & Dissident Tactics and Strategies in South & Southeast Asia.
2006-08 National Science Foundation Grant SES-0619997. Research Experience for Undergraduates (REU) Supplement: Modeling Intranational Conflict-Cooperation Processes.
2005-08 National Science Foundation Grant, SES-0516545 & SES-0452769. Project Civil Strife: Multi-Actor Models of Intranational Conflict & Cooperation.
2002-04 National Science Foundation Grant, SES-0214287. Doctoral Dissertation Research in Political Science: Taking Turns: A Theory and A Model of State-Dissident Interactions.
Indirect Funding (Increased accuracy of software and dictionaries)
2012-13 Office of Naval Research. “Worldwide Integrated Crisis Early Warning System (W-ICEWS).”
2012-13 Office of Naval Research. “Subregional Modeling of Political Conflict and Instability.”
2010-13 Office of Naval Research. “Turning Text into Behavioral Processes and Public Support: Supporting the Next Generation of Conflict Analysis.”
2009-11 Defense Advanced Research Projects Agency (DARPA), Subcontracted through Lockheed Martin . “Integrated Crisis Early Warning System (ICEWS) – Phase III.”
2009-11 Defense Advanced Research Projects Agency (DARPA), Subcontracted through Lockheed Martin. “Integrated Crisis Early Warning System (ICEWS) – Phase II.”
2008-09 Defense Advanced Research Projects Agency. Automated Sentiment Analysis. August 15 – February 15.
2007-08 Defense Advanced Research Projects Agency (DARPA), Subcontracted through Lockheed Martin . “Integrated Crisis Early Warning System (ICEWS) – Phase I.”
2008-2013 Strategic Analysis Enterprises. Internal Research and Development for Xenophon, Taxis, Logos, and Pathos.
Xenophon is a software program developed to code “who is doing what to whom” within countries. The program was developed by Strategic Analysis Enterprises, a private corporation, and licensed to William & Mary to code these data. More information can be obtained by contacting SAE (www.strategicanalysisenterprises.com). In multiple random samples, the automated event data were hand graded for accuracy and recall. The date, source, actor, target, and event all had to be correct for a grader to mark an observation correct. The data are 70% accurate (+/-3 points depending on the sample). In a recall study, coders read reports and hand coded event data. These data were then compared to the output from Xenophon on the same stories. The recall scores were greater than 50% (greater than 50% of the events found by humans were coded by Xenophon). These values to our knowledge are the highest accuracy and recall scores reported for publicly available event data. It is a long painstaking process to hand code and hand grade events and machine output and not many researchers producing such data go through this process. We can say with confidence our data are among the most accurate, if not the most accurate, event data publicly available today.
The actor dictionaries are also a culmination of efforts over the last 5-7 years. They were begun by undergraduate students at the College of William & Mary and the University of Georgia. But along the way as they were used in multiple government projects benefitted from assistance through Phil Schrodt’s teams at both the University of Kansas and the Pennsylvania State University. In addition, as part of the Integrated Crisis Early Warning System (ICEWS) project, the dictionaries were also modified and maintained by Lockheed Martin researchers. Given all the work on these dictionaries through the years, they are deemed to be some of the most complete dictionaries ever produced especially for this region.
How these data differ from the ICEWS Data
These data are not direct products of the ICEWS project. In particular, they are coded using a completely different engine, Xenophon. They also adopt a different actor scheme. However, they correlate very highly to the ICEWS data in the sense that the data come from virtually the same set of documents and are generated by a coder that yields similar accuracies to Raytheon-BBN’s Serif coder which was used to produce the ICEWS data.
- Introduction to what’s here (Readme file)
- PCS Event Data Guide
- PCS Actor Codes Guide
- PCS Research Note
- CAMEO Event Code Manual
Datasets and Actor Dictionaries