No abstract

Entries in the burgeoning “text-as-data” movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into another analysis (like locating parties in ideological space) or both. We discuss a variety of techniques for selecting words that capture partisan, or other, differences in political speech and for evaluating the relative importance of those words. We introduce and emphasize several new approaches based on Bayesian shrinkage and regularization. We illustrate the relative utility of these approaches with analyses of partisan, gender, and distributive speech in the U.S. Senate.

From Syria to Sudan, governments have informal ties with militias that use violence against opposition groups and civilians. Building on research that suggests these groups offer governments logistical benefits in civil wars as well as political benefits in the form of reduced liability for violence, we provide the first systematic global analysis of the scale and patterns of these informal linkages. We find over 200 informal state-militia relationships across the globe, within but also outside of civil wars. We illustrate how informal delegation of violence to these groups can help some governments avoid accountability for violence and repression. Our empirical analysis finds that weak democracies as well as recipients of financial aid from democracies are particularly likely to form informal ties with militias. This relationship is strengthened as the monitoring costs of democratic donors increase. Out-of-sample predictions illustrate the usefulness of our approach that views informal ties to militias as deliberate government strategy to avoid accountability.

This article presents ViEWS – a political violence early-warning system that seeks to be maximally transparent, publicly available, and have uniform coverage, and sketches the methodological innovations required to achieve these objectives. ViEWS produces monthly forecasts at the country and subnational level for 36 months into the future and all three UCDP types of organized violence: state-based conflict, non-state conflict, and one-sided violence in Africa. The article presents the methodology and data behind these forecasts, evaluates their predictive performance, provides selected forecasts for October 2018 through October 2021, and indicates future extensions. ViEWS is built as an ensemble of constituent models designed to optimize its predictions. Each of these represents a theme that the conflict research literature suggests is relevant, or implements a specific statistical/machine-learning approach. Current forecasts indicate a persistence of conflict in regions in Africa with a recent history of political violence but also alert to new conflicts such as in Southern Cameroon and Northern Mozambique. The subsequent evaluation additionally shows that ViEWS is able to accurately capture the long-term behavior of established political violence, as well as diffusion processes such as the spread of violence in Cameroon. The performance demonstrated here indicates that ViEWS can be a useful complement to non-public conflict-warning systems, and also serves as a reference against which future improvements can be evaluated.