Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-n research initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.

This paper studies the impact of food insecurity on civilian–rebel interactions. We argue that food price volatilities affect the incentives of insurgent groups and their subsequent treatment of civilians. The hypotheses developed in this study are empirically evaluated across a battery of statistical models using monthly data from a sample of 112 first administrative districts in sub-Saharan Africa. The results show that increases in food insecurity substantially raise the likelihood of insurgent groups committing violence against civilians and that districts with a higher proportion of agricultural land are at greatest risk of civilian victimization by rebel groups during these episodes of food insecurity. The implications of this analysis suggest that the human impact of food insecurity does not simply relate to nutrition and questions of governance. Food price volatilities also incentivize the use of violence against civilians by non-state actors, which is a pertinent concern of human rights organizations and policymakers.

Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.

Current climate change research suggests that certain seasonal weather patterns will be extended and others attenuated as global temperature increases. This is important because seasonal temperature change affects both the scarcity of resources during certain times of the year and the overall mobility of people living in countries that have seasonality. Consequently, these seasonal changes have implications for the onset of violent conflict, particularly as it relates to distinguishing when, where, and how it is most likely to occur. This article evaluates the relationship between monthly temperature changes, civil war onset, and various, less-organized conflict events, offering theoretical expectations for how seasonal changes and climate aberrations are related to an increased risk of violence. The results show that prolonged periods of stable, warm weather are consistently associated with an increased risk of civil war onset and non-state conflict. These findings are best explained through the strategic viability mechanism of temperature change, which allows actors to resolve their collective action problems that are often associated with poor weather conditions, while simultaneously increasing their strategic and behavioral incentives for engaging in violent conflict. Warm weather generates more resources for rebel looting and permits predictability for coordinating troop movements and strategy development. These findings are particularly salient in areas of the world affected by strong seasonality, where prolonged extensions of warm weather conditions would be regarded as both peculiar and attractive for participating in violent action. Although these findings are notable, even under the most extreme climate change scenarios, the substantive effects for these relationships are comparatively minor relative to other well-known intrastate conflict covariates.

Media data has been the subject of large scale analysis with applications of text mining being used to provide overviews of media themes and information flows. Such information extracted from media articles has also shown its contextual value of being integrated with other data, such as criminal records and stock market pricing. In this work, we explore linking textual media data with curated secondary textual data sources through user-guided semantic lexical matching for identifying relationships and data links. In this manner, critical information can be identified and used to annotate media timelines in order to provide a more detailed overview of events that may be driving media topics and frames. These linked events are further analyzed through an application of causality modeling to model temporal drivers between the data series. Such causal links are then annotated through automatic entity extraction which enables the analyst to explore persons, locations, and organizations that may be pertinent to the media topic of interest. To demonstrate the proposed framework, two media datasets and an armed conflict event dataset are explored.