Suchergebnisse

"Die sozialen Prozesse, die Konflikt hervorbringen, arbeiten auf mehreren Ebenen gleichzeitig. Integrierte Erklärungsansätze müssen deshalb notwendigerweise mehrere Analyseebenen einschließen. Dieser Aufsatz untersucht die Schwierigkeiten, die die empirische Prüfung auf mehreren Ebenen mit sich bringt. Weder aggregierte Studien auf der Staatsebene, noch disaggregierte Analysen können ein vollständiges Bild liefern, da Bürgerkriege das Ergebnis des Zusammenwirkens verschiedener Ebenen sind. Der Autor entwickelt eine Typologie verschiedener Strategien für die empirische Überprüfung von Mehrebenen-Theorien und unterscheide dabei zwischen Einzel- und Mehrebenen-Messung und -Test. Der Aufsatz illustriert mit einem Beispiel, wie eine Mehrebenen-Analyse durchgeführt werden kann. Er zeigt, wie mit Hilfe von geographischer Software Variablen auf mehreren Ebenen berechnet werden können, welche dann in einem hierarchischen statistischen Modell getestet werden." (Autorenreferat)

The literature suggests that geographically concentrated groups face a higher likelihood of conflict. While this finding seems to be commonly accepted, there is no clear consensus that explains why this is the case. Two competing mechanisms have been proposed: first, a motivation-driven mechanism, where the existence of a well-defined group territory makes the group more likely to fight for it; and second, an opportunity-driven link, where concentration facilitates group coordination for collective action. This article aims to resolve this controversy by developing new settlement pattern indicators based on Geographic Information Systems (GIS) data. Using conflict data at the level of ethnic groups, I show that there is clear evidence in favor of the opportunity mechanism. Thus, the effect of group concentration on conflict seems to be driven by the strategic advantages for group coordination that the spatial proximity of group members provides.

With the increasing relevance of ethnic groups as political actors, the literature has attempted to identify and study the ethnic organizations representing these groups. How do these organizations use digital communication channels to reach their domestic and international audiences? To enable research on these questions, this article introduces the Ethnic Organizations Online dataset, a new data collection focusing on the online channels that ethnic organizations use. The dataset includes four types of channels: Twitter (since July 2023, rebranded by Elon Musk as X); Facebook; Instagram; and regular websites. It relies on the Ethnic Power Relations – Organizations database, and is therefore compatible with an entire family of datasets on ethnic politics. Featuring more than 2000 online channels used by 265 groups, it allows researchers to study a wide variety of questions related to digital ethnic mobilization. The article presents three examples of how the dataset can be used. We study: (a) how a group's political goals influence social media adoption; (b) how elections impact the organizations' communication frequency and how this differs between democracies and autocracies; and (c) how the power status of a group affects the content of their communication. We provide replication codes facilitating the use of the dataset in applied research.

Autocratic governments routinely interfere in digital communication technology for political purposes. However, citizens can use different technologies to bypass government interference. This article examines how political protest influences the use of anonymity-preserving digital services in autocracies. Citizens should be more likely to use these tools during high political tension because they fear governmental surveillance or censorship. The analysis combining data on the Tor anonymization network with protest event data demonstrates noticeable increases in Tor usage after days with many protest events but not days with single protest events.

A growing body of research has studied how autocratic regimes interfere with internet communication to contain challenges to their rule. In this review article, we survey the literature and identify the most important directions and challenges for future research. We structure our review along different network layers, each of which provides particular ways of governmental influence and control. While current research has made much progress in understanding individual digital tactics, we argue that there is still a need for theoretical development and empirical progress. First, we need a more comprehensive understanding of how particular tactics fit into an overall digital strategy, but also how they interact with traditional, "offline" means of autocratic politics, such as cooptation or repression. Second, we discuss a number of challenges that empirical research needs to address, such as the effectiveness of digital tactics, the problem of attribution, and the tool dependence of existing research.

Protest against authoritarian rule is a well-studied phenomenon in the social sciences, but mass rallies in favor of authoritarian regimes have received only limited scholarly attention. While previous work has portrayed authoritarian regimes as characterized by mass apathy and political demobilization, we show that this is only partially true today. We argue that autocrats mobilize their supporters selectively as a strategic response to political threats. Rallies increase collective action costs for rivaling elites, opposition movements, and bystanders because they signal regime strength (deterrence) and curb mobilization efforts against the regime (repression). Nevertheless, the mobilization of supporters is costly, as autocrats have only imperfect information about current levels of support, rallies require organizational capacity and clashes between supporters and opponents can get out of control. Drawing on the first global data set with information about pro-government rally events in all authoritarian regimes from 2003 to 2015, our quantitative analysis reveals systematic patterns in the occurrence of rallies in line with our theoretical framework. We find systematic increases in pro-government mobilization during episodes of large domestic and regional opposition mobilization, high coup risk, and prior to elections.

Existing research has shown that online networks are often segregated along identity lines, such as political ideology or religious views. Although online segregation should be specifically detrimental when appearing between ethnic groups in a post-conflict setting, to date we have no systematic evidence on the level of online ethnic segregation. To close this gap, the present study examines online ethnic segregation in a large ethnically mixed blogger network in a post-conflict society, Bosnia and Herzegovina. Since politics has been found to enhance ethnic divides in the offline world, we additionally examine whether segregation is higher for bloggers engaging with political topics. Using large-scale web scraping, automated text analysis and Monte Carlo simulation, we find evidence for pronounced ethnic divisions. Furthermore, we find that political bloggers tend to have more ethnically segregated networks. The findings show that a broad public exchange transcending ethnic categories remains limited in the online context we study, and that those who dominate the online political debate tend to be those who in their social interactions put even more weight on ethnic categories than the average.

Nighttime illumination can serve as a proxy for economic variables in particular in developing countries, where data are often not available or of poor quality. Existing research has demonstrated this for coarse levels of analytical resolution, such as countries, administrative units or large grid cells. In this article, we conduct the first fine-grained analysis of night lights and wealth in developing countries. The use of large-scale, geo-referenced data from the Demographic and Health Surveys allows us to cover 39 less developed, mostly non-democratic countries with a total sample of more than 34,000 observations at the level of villages or neighborhoods. We show that light emissions are highly accurate predictors of economic wealth estimates even with simple statistical models, both when predicting new locations in a known country and when generating predictions for previously unobserved countries.

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.