We need to go deeper: measuring electoral violence using convolutional neural networks and social media

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology

Replication Data for: Comparing Random Forests with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

This is the replication data for the article "Comparing Random Forests with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

Introducing the Targeted Mass Killing Data Set for the Study and Forecasting of Mass Atrocities

This article describes a new data set for the study of genocide, politicide, and similar atrocities. Existing data sets have facilitated advances in understanding and policyrelevant applications such as forecasting but have been criticized for insufficient transparency, replicability, and for omitting failed or prevented attempts at genocide/ politicide. More general data sets of mass civilian killing do not typically enable users to isolate situations in which specific groups are deliberately targeted. The Targeted Mass Killing (TMK) data set identifies 201 TMK episodes, 1946 to 2017, with annualized information on perpetrator intent, severity, targeted groups, and new ordinal and binary indicators of genocide/politicide that can serve as alternatives to existing measures. Users are also able to construct their own indicators based on their research questions or preferred definitions. The article discusses the concept and operationalization of TMK, provides comparisons with other data sets, and highlights some of the strengths and new capabilities of the TMK data.Australian Research Council (Grant ID: DP160101122), University of Otago Research Grant (2015)

Introducing the Targeted Mass Killing Dataset for the Study and Forecasting of Mass Atrocities

This article describes a new data set for the study of genocide, politicide, and similar atrocities. Existing data sets have facilitated advances in understanding and policy-relevant applications such as forecasting but have been criticized for insufficient transparency, replicability, and for omitting failed or prevented attempts at genocide/politicide. More general data sets of mass civilian killing do not typically enable users to isolate situations in which specific groups are deliberately targeted. The Targeted Mass Killing (TMK) data set identifies 201 TMK episodes, 1946 to 2017, with annualized information on perpetrator intent, severity, targeted groups, and new ordinal and binary indicators of genocide/politicide that can serve as alternatives to existing measures. Users are also able to construct their own indicators based on their research questions or preferred definitions. The article discusses the concept and operationalization of TMK, provides comparisons with other data sets, and highlights some of the strengths and new capabilities of the TMK data