Question: Is event data useful in forecasting models of conflict?
Evidence: Political event data has existed in various forms since the 1970s. Two of the most common political event datasets were the World Event Interaction Survey (WEIS) and the Conflict and Peace Data Bank (COPDAB) BIBREF0 , BIBREF1 . These two datasets were eventually replaced by the projects created by Philip Schrodt and various collaborators. In general, these projects were marked by the use of the Conflict and Mediation Event Observations (CAMEO) coding ontology and automated, machine-coding rather than human coding BIBREF2 , BIBREF3 . The CAMEO ontology is made up of 20 “top-level” categories that encompass actions such as “Make Statement” or “Protest”, and contains over 200 total event classifications. This ontology has served as the basis for most of the modern event datasets such as the Integrated Crisis Early Warning System (ICEWS) BIBREF4 , the Global Database of Events, Language, and Tone (GDELT), and the Phoenix dataset presented in this paper.
This type of data can prove highly useful for many types of studies. Since this type of data is inherently atomic, each observation is a record of a single event between a source and a target, it provides a disaggregated view of political events. This means that the data can be used to examine interactions below the usual monthly or yearly levels of aggregation. This approach can be used in a manner consistent with traditional hypothesis testing that is the norm in political science BIBREF5 , BIBREF6 , BIBREF7 . Additionally, event data has proven useful in forecasting models of conflict since the finer time resolution allows analysts to gain better leverage over the prediction problem than is possible when using more highly aggregated data BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . Finally, the advent of daily-updated event data has led to many novel uses such as watchboarding or dashboarding. The goal in these situations is to provide an easy to understand interface that analysts can use to quickly monitor ongoing or emerging situations around the world. These applications provide a new frontier for event data that has not been considered much until this point.
The status quo of TABARI-generated, CAMEO-coded event data, which was established in the early 2000s, has remained with little change. BIBREF12 outlined many potential advances in the generation of political event data. These advances are things such as realtime processing of news stories, the incorporation of open-source natural language processing (NLP) software, and enhancements in the automated coding structure. Two publicly-available datasets, GDELT and ICEWS, have each attempted to implement some, or all, of these changes in their respective data-generating pipelines. In terms of goals, the ICEWS project seems closest to sharing the vision of the Phoenix dataset. A more in-depth comparison of Phoenix and ICEWS is presented in a later section. In short, the goal of the project presented in this chapter is to implement most of the improvements suggested in BIBREF12 .
Additionally, event data has proven useful in forecasting models of conflict since the finer time resolution allows analysts to gain better leverage over the prediction problem than is possible when using more highly aggregated data (Arva, Beieler, Fisher, Lara, Schrodt, Song, Sowell and Stehle 2013; Brandt, Freeman and Schrodt 2014; Brandt, Freeman, min Lin and Schrodt 2013; Brandt, Freeman and Schrodt 2011).
Answer: Yes.