Fragility and Destabilizing Protest: Combining Event and Structural Data for Improved Forecasts

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Social science-based efforts to achieve success forecasting events of interest, including domestic political crises in foreign countries, have advanced considerably in recent years, though controversy exists over claims of success, the validity of methods, and the credibility of codings. One issue that challenges every big data effort is the verifiable operationalization of concepts. That entails devising and deploying understandable and clearly codable proxies, or indicators, that do not distort the effective meaning of the variable being operationalized. An important example of these efforts is the W-ICEWS project funded originally by DARPA and then OSD/ONR. Organized as a team effort by Lockheed-Martin, ATL, the project has sought to improve its performance on a variable known as “Domestic Political Crisis.” Difficulties defining that variable have suggested the possibility of narrowing its focus to “Destabilizing Protest”–the occurrence of mainly non-violent unrest that threatens reigning institutions of authority seriously enough to warrant high-level attention from US policy makers. In this paper we report on efforts to develop this variable by combining publicly available assessments of regime fragility with event data measuring mass protest from the W-ICEWS project. We then test the viability of this new variable by making in-sample and out-of-sample forecasts using a multivariate model of social indicators.