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Manual coding of political events from news reports is extremely expensive and time-consuming, whereas completely automatic coding has limitations when it comes to the precision and granularity of the data collected. In this paper, we introduce an alternative strategy by establishing a semi-automatic pipeline, where an automatic classification system eliminates irrelevant source material before further coding is done by humans. Our pipeline relies on a high-performance supervised heterogeneous ensemble classifier working on extremely unbalanced training classes. Deployed to the Mass Mobilization on Autocracies database on protest, the system is able to reduce the number of source articles to be human-coded by more than half, while keeping over 90% of the relevant material.

Machine-coded datasets likely represent the future of event data analysis. We assess the use of one of these datasets—Global Database of Events, Language and Tone (GDELT)—for the micro-level study of political violence by comparing it to two hand-coded conflict event datasets. Our findings indicate that GDELT should be used with caution for geo-spatial analyses at the subnational level: its overall correlation with hand-coded data is mediocre, and at the local level major issues of geographic bias exist in how events are reported. Overall, our findings suggest that due to these issues, researchers studying local conflict processes may want to wait for a more reliable geocoding method before relying too heavily on this set of machine-coded data.

The implementation of the United States military surge in Iraq coincided with a significant reduction in ethnic violence. Two explanations have been proposed for this result: The first is that the troop surge worked by increasing counterinsurgent capacity, whereas the second argument is that ethnic unmixing and the establishment of relatively homogenous enclaves were responsible for declining violence in Baghdad through reducing contact. We address this question using an agent-based model that is built on GIS-coded data on violence and ethnic composition in Baghdad. While we cannot fully resolve the debate about the effectiveness of the surge, our model shows that patterns of violence and segregation in Baghdad are consistent with a simple mechanism of ethnically motivated attacks and subsequent migration. Our modeling exercise also informs current debates about the effectiveness of counterinsurgency operations. We implement a simple policing mechanism in our model and show that even small levels of policing can dramatically mitigate subsequent levels of violence. However, our results also show that the timing of these efforts is crucial; early responses to ethnic violence are highly effective, but quickly lose impact as their implementation is delayed. Adapted from the source document.

What explains local variation in electoral manipulation in countries with ongoing internal conflict? The theory of election fraud developed in this article relies on the candidates' loyalty networks as the agents manipulating the electoral process. It predicts (i) that the relationship between violence and fraud follows an inverted U-shape and (ii) that loyalty networks of both incumbent and challenger react differently to the security situation on the ground. Disaggregated violence and election results data from the 2009 Afghanistan presidential election provide empirical results consistent with this theory. Fraud is measured both by a forensic measure, and by using results from a visual inspection of a random sample of the ballot boxes. The results align with the two predicted relationships, and are robust to other violence and fraud measures. Adapted from the source document.

What explains local variation in electoral manipulation in countries with ongoing internal conflict? The theory of election fraud developed in this article relies on the candidates' loyalty networks as the agents manipulating the electoral process. It predicts (i) that the relationship between violence and fraud follows an inverted U-shape and (ii) that loyalty networks of both incumbent and challenger react differently to the security situation on the ground. Disaggregated violence and election results data from the 2009 Afghanistan presidential election provide empirical results consistent with this theory. Fraud is measured both by a forensic measure, and by using results from a visual inspection of a random sample of the ballot boxes. The results align with the two predicted relationships, and are robust to other violence and fraud measures. (British Journal of Political Science/ FUB)

What explains local variation in electoral manipulation in countries with ongoing internal conflict? The theory of election fraud developed in this article relies on the candidates' loyalty networks as the agents manipulating the electoral process. It predicts (i) that the relationship between violence and fraud follows an inverted U-shape and (ii) that loyalty networks of both incumbent and challenger react differently to the security situation on the ground. Disaggregated violence and election results data from the 2009 Afghanistan presidential election provide empirical results consistent with this theory. Fraud is measured both by a forensic measure, and by using results from a visual inspection of a random sample of the ballot boxes. The results align with the two predicted relationships, and are robust to other violence and fraud measures.