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Political scientists and security agencies increasingly rely on computerized event data generation to track conflict processes and violence around the world. However, most of these approaches rely on pattern-matching techniques constrained by large dictionaries that are too costly to develop, update, or expand to emerging domains or additional languages. In this paper, we provide an effective solution to those challenges. Here we develop the 3M-Transformers (Multilingual, Multi-label, Multitask) approach for Event Coding from domain specific multilingual corpora, dispensing external large repositories for such task, and expanding the substantive focus of analysis to organized crime, an emerging concern for security research. Our results indicate that our 3M-Transformers configurations outperform state-of-the-art usual Transformers models (BERT and XLM-RoBERTa) for coding events on actors, actions and locations in English, Spanish, and Portuguese languages. 

Extracting structured metadata from unstructured text in different domains is gaining strong attention from multiple research communities. In Political Science, these metadata play a significant role on studying intra and inter-state interactions between political entities. The process of extracting such metadata usually relies on domain specific ontologies and knowledge-based repositories. In particular, Political Scientists regularly use the well-defined ontology CAMEO, which is designed for capturing conflict and mediation relations. Since CAMEO repositories are currently human maintained, the high cost and extensive human effort associated with updating them makes it difficult to include new entries on a regular basis. This paper introduces HANKE: an innovative framework for automatically extracting knowledge representations from unstructured sources, in order to extend CAMEO ontology both in the same domain and towards other related domains in political science. HANKE combines Hierarchical Attention Networks as engine for identifying relevant structures in raw-text and the novel Frequency-Based Ranker approach to obtain a collection of candidate entries for CAMEO's repositories. To show the efficiency of the proposed framework, we evaluate its performance on capturing existing CAMEO representations in a soft-labelled dataset. We also empirically demonstrate the versatility and superiority of HANKE method by applying it to two case studies related to CAMEO extension on its actual domain and towards organized crime domain. 

Today, Spanish speaking countries face widespread political crisis. These political conflicts are published in a large volume of Spanish news articles from Spanish agencies. Our goal is to create a fully functioning system that parses realtime Spanish texts and generates scalable event code. Rather than translating Spanish text into English text and using English event coders, we aim to create a tool that uses raw Spanish text and Spanish event coders for better flexibility, coverage, and cost.To accommodate the processing of a large number of Spanish articles, we adapt a distributed framework based on Apache Spark. We highlight how to extend the existing ontology to provide support for the automated coding process for Spanish texts. We also present experimental data to provide insight into the data collection process with filtering unrelated articles, scaling the framework, and gathering basic statistics on the dataset. 

Abstract This study examines determinants of leftist violence at the municipal level in Colombia from 2000 through 2010. A multilevel GLMM model with a negative binomial distribution is used to take advantage of the information available at the municipal and department level. Surprisingly, inequality was not a significant covariate of violence, and agricultural GDP tended to reduce, instead of increase, guerrilla violence. The main risk factors identified include physical characteristics such as rugged topography and prior violence, but also factors that are candidates for policy action, such as unemployment, incorporation of the poor into public services, repression, and the energy and mining sector. These findings suggest interventions to decrease risks of guerrilla violence beyond merely strengthening the state. While repression tends to escalate violence, targeted policies to provide health benefits to those currently underserved, and securing mining and oil operations can effectively reduce the risk of violence.