PLOVER and POLECAT: A New Political Event Ontology and Dataset

Abstract

POLECAT is a new global political event dataset intended to serve as the successor to the dataset produced by the DARPA Integrated Conflict Early Warning System (ICEWS) project. POLECAT's event data are machine coded from millions of multi-language international news reports and will soon cover the period 2010-to-present. These data are generated using the Next Generation Event Coder (NGEC), a new automated coder that replaces the use of extensive (and difficult to update) dictionaries with a more flexible set of expert annotations of an event's characteristics. In contrast to existing automated event coders, it uses a combination of NLP tools, transformer-based neural networks, and actor information sourced from Wikipedia. POLECAT's event data are based on an event-mode-context ontology, the Political Language Ontology for Verifiable Event Records (PLOVER), that replaces the older CAMEO ontology used in past datasets such as ICEWS and Phoenix. These innovations offer substantial improvements in the scope and accuracy of political event data in terms of the what, how, why, where, and when of domestic and international interactions. After detailing PLOVER and POLECAT, we illustrate the innovations and improvements through a preliminary comparison to the existing-ICEWS event data system.

Introducing a global dataset on conflict forecasts and news topics

“…This is because the project does not impose what kind of news content or events will be indicative of conflict risk. This is a significant difference from the approach taken in the Political Instability Task Force (PITF), for example, which relies on event coders to produce large databases like the ICEWS database from Boschee et al (2015) or, more recently, the Polecat global event dataset (Halterman et al, 2023). We expect the NLP approach here to work better if the factors that predict conflict, especially those that are negatively associated with conflict risk, are hard to foresee.…”