Communication Technology and Reports on Political Violence : Cross-National Evidence Using African Events Data

The spread of Internet and mobile phone access around the world has implications for both the processe s of contentious politics and subsequent reporting of protest, terrorism, and war. In this paper, we explore whether political violent events that occur close to modern communication networks are systematically better reported than others. Our analysis app roximates information availability by the level of detail provided about the date of each political violent event in Africa from 2008 to 2010 and finds that although access to communication technology improves reporting, the size of the effect is very smal l. Additional investigation finds that the effect can be attributed to the ability of journalists to access more diverse primary sources in remote areas due to increased local access to modern communication technology

Reporting of Non-Fatal Conflict Events

Temporally and spatial disaggregated datasets are commonly used to study political violence. Researchers are increasingly studying the data generation process itself to understand the selection processes by which conflict events are included in conflict datasets. This work has focused on conflict fatalities. In this research note, we explore how non-fatal conflict events are reported upon and enter into datasets of armed conflict. To do so, we compare reported non-fatal conflict events with the population of events in two direct observation datasets, collected using a boots-on-the-ground strategy: mass abductions in Nepal (1996-2006) and troop movements in Darfur. We show that at the appropriate level of aggregation media reporting on abductions in Nepal largely mirrors the "true" population of abductions, but at more disaggregated levels of temporal or spatial analysis, the match is poor. We also show that there is no overlap between a media-driven conflict dataset and directly-observed data on troop movements in Sudan. These empirics indicate that non-fatal data can suffer from serious underreporting and that this is particularly the case for events lacking elements of coercion. These findings are indicative of selection problems in regards to the reporting on non-fatal conflict events. Los conjuntos de datos desagregados temporal y espacialmente se utilizan de manera habitual para analizar la violencia politica. Los investigadores estudian cada vez mas el proceso de generacion de datos para comprender los procesos de seleccion a traves de los que se incluyen eventos de conflicto en los conjuntos de datos sobre conflictos. Este trabajo se centro en las victimas fatales resultantes de los conflictos. En esta nota de investigacion, exploramos como se informan los acontecimientos conflictivos no fatales y como se incorporan esos datos al conjunto de datos sobre conflictos armados. Para ello, comparamos los casos informados de acontecimientos conflictivos no fatales con el resto de los acontecimientos en dos conjuntos de datos de observacion directa, recopilados mediante una estrategia de observacion sobre el terreno: los secuestros masivos en Nepal (1996-2006) y los movimientos de tropas en Darfur. Demostramos que, en el nivel adecuado de agregacion de datos, la informacion de los medios de comunicacion sobre los secuestros en Nepal refleja en gran medida la verdadera cantidad de secuestros, pero, en niveles mas desagregados del analisis temporal o espacial, la coincidencia es deficiente. Tambien demostramos que no hay coincidencia entre un conjunto de datos sobre conflictos impulsados por los medios de comunicacion y los datos recopilados mediante la observacion directa durante los movimientos de tropas en Sudan. Estos datos empiricos indican que los casos de victimas no fatales presentan un problema grave de infradeclaracion, en especial, en hechos que carecen de elementos de coercion. Estos resultados indican problemas de seleccion en lo que respecta a la declaracion de acontecimientos conflictivos no fatales. Des jeux de donnees desagregees dans le temps et dans l'espace sont couramment utilises pour etudier la violence politique. Des chercheurs etudient de plus en plus le processus de generation de donnees en lui-meme pour comprendre les processus de selection par lesquels les evenements de conflits sont inclus dans les jeux de donnees sur les conflits. Mais ce travail se concentre sur les deces lies aux conflits. Dans cet expose de recherche, nous etudions la maniere dont les evenements de conflits non mortels sont rapportes et inclus dans les jeux de donnees sur les conflits armes. Pour ce faire, nous comparons les evenements de conflits non rapportes avec la population des evenements de deux jeux de donnees d'observation directe recueillies a l'aide d'une strategie de terrain : l'un de ces jeux de donnees concerne enlevements de masse au Nepal (1996-2006) et l'autre concerne les mouvements de troupes au Darfour. Nous montrons qu'au niveau d'agregation approprie, les reportages des medias sur les enlevements au Nepal refletent en grande partie la population reelle victime d'enlevement, mais qu'a des niveaux plus desagreges d'analyse dans le temps ou dans l'espace, la concordance est faible. Nous montrons egalement qu'il n'y a pas de recoupement entre le jeu de donnees sur les conflits alimente par les medias et les donnees acquises par observation directe des mouvements de troupes au Soudan. Ces analyses empiriques indiquent que les donnees sur les evenements non mortels souffrent d'une grave sous-declaration et que c'est particulierement le cas pour les evenements depourvus d'elements de coercition. Ces resultats indiquent des problemes de selection en ce qui concerne les rapports sur les evenements de conflits non mortels

Improving the selection of news reports for event coding using ensemble classification

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

Replication Data for: Improving the Selection of News Reports for Event Coding Using Ensemble Classification

We introduce an automatic classification system to eliminate irrelevant source material for the coding of political event data from global news-wires. Our pipeline relies on a high-performance supervised heterogeneous ensemble classifier working on extremely unbalanced training classes. The output is then supplied to human coders for further information extraction, creating a semi-automatic pipeline. The package includes the software required to train and test the classifier, as well as documentation on how to use it

A Collaborative GIS Solution for Public Transport

The recent years brought forward a large number of solutions for automating route finding given the increased availability of geographical data. However, such solutions rarely focus on mass transit or involve the user in submitting information in a collaborative manner to further improve the available dataset and provide additional services. The system presented here intends to fully address these issues by providing a modular, extensible collaborative one-stop-shop for public transport needs based on multi-source collaborative data inputs from both official and user-submitted sources with the usage of a flexible, genetic-algorithms based route-finding application. Implementation wise, the solution is based on an open-ended system of collaborative web-services with front-ends available on mobile, desktop and web platforms. The proposed solution will not only provide users with a powerful technical solution, but will address the theoretical concern by which the increase of available GIS data is solely used for last-mile, map-like solution

External support in armed conflicts : Introducing the UCDP external support dataset (ESD), 1975-2017

In this article, we present the most up-to-date, fine-grained, global dataset on external support in armed conflicts: the UCDP External Support Dataset (ESD). The dataset encompasses data on states and non-state actors as both supporters and recipients and provides detailed information on the type of support provided to warring parties in armed conflicts between 1975 and 2017. We use it to highlight three broader trends in the provision of external support: (1) a dramatic increase in the number of external supporters, (2) a larger share of pro-government interventions, and (3) the rise of direct military intervention as the predominant mode of external support. In conclusion, we identify several avenues worthy of future inquiry that could significantly improve our understanding of external support in armed conflicts

Introducing the UCDP Candidate Events Dataset

This article presents a new, monthly updated dataset on organized violence—the Uppsala Conflict Data Program Candidate Events Dataset. It contains recent observations of candidate events, a majority of which are eventually included in the Uppsala Conflict Data Program Georeferenced Event Dataset as part of its annual update after a careful vetting process. We describe the definitions, sources and procedures employed to code the candidate events, and a set of issues that emerge when coding data on organized violence in near-real time. Together, the Uppsala Conflict Data Program Candidate and Georeferenced Event Datasets minimize an inherent trade-off between update speed and quality control. Having monthly updated conflict data is advantageous for users needing near-real time monitoring of violent situations and aiming to anticipate future developments. To demonstrate this, we show that including them in a conflict forecasting system yields distinct improvements in terms of predictive performance: Average precision increases by 20–40% relative to using the Uppsala Conflict Data Program Georeferenced Event Dataset only. We also show that to ensure quality and consistency, revisiting the initial coding making use of sources that become available later is absolutely necessary

Civil conflict sensitivity to growing-season drought

To date, the research community has failed to reach a consensus on the nature and significance of the relationship between climate variability and armed conflict. We argue that progress has been hampered by insufficient attention paid to the context in which droughts and other climatic extremes may increase the risk of violent mobilization. Addressing this shortcoming, this study presents an actor-oriented analysis of the drought-conflict relationship, focusing specifically on politically relevant ethnic groups and their sensitivity to growing-season drought under various political and socioeconomic contexts. To this end, we draw on new conflict event data that cover Asia and Africa, 1989-2014, updated spatial ethnic settlement data, and remote sensing data on agricultural land use. Our procedure allows quantifying, for each ethnic group, drought conditions during the growing season of the locally dominant crop. A comprehensive set of multilevel mixed effects models that account for the groups' livelihood, economic, and political vulnerabilities reveals that a drought under most conditions has little effect on the short-term risk that a group challenges the state by military means. However, for agriculturally dependent groups as well as politically excluded groups in very poor countries, a local drought is found to increase the likelihood of sustained violence. We interpret this as evidence of the reciprocal relationship between drought and conflict, whereby each phenomenon makes a group more vulnerable to the other

Climate variability, crop and conflict : Exploring the impacts of spatial concentration in agricultural production

Although substantive agreement exists on the role of climate variability and food scarcity in increasing violence, a limited number of studies have investigated how food resources affect violent conflict. This article explores the complex linkages between climate variability, agricultural production and conflict onset, by focusing on the spatial distribution of crop production in a cross-country setting. We hypothesize that spatial differences in crop production within countries are a relevant factor in shaping the impact of climate variability on conflict in agriculturally -dependent countries. To test this hypothesis, we rely on high-resolution global gridded data on the local yield of four main crops for the period 1982–2015 and aggregate the grid-cell information on crop production to compute an empirical indicator of the spatial concentration of agricultural production within countries. Our results show that the negative impacts of climate variability lead to an increase in the spatial concentration of agricultural production within countries. In turn, the combined effect of climate extremes and crop production concentration increases the predicted probability of conflict onset by up to 14% in agriculturally dependent countries.CLIMSECENERGY

A conditional model of local income shock and civil conflict

Common political economy models point to rationalist motives for engaging in conflict but say little about how income shocks translate into collective violence in some cases but not in others. Grievance models, in contrast, focus on structural origins of shared frustration but offer less insight into when the deprived decide to challenge the status quo. Addressing these lacunae, we develop a theoretical model of civil conflict that predicts income loss to trigger violent mobilization primarily when the shock can be linked to preexisting collective grievances. The conditional argument is supported by results of a comprehensive global statistical analysis of conflict involvement among ethnic groups. Consistent with theory, we find that this relationship is most powerful among recently downgraded groups, especially in the context of agricultural dependence and low local level of development, whereas political downgrading in the absence of adverse economic changes exerts less influence on ethnic conflict risk