Department for Computational Social Science

Abstract

To mitigate the spread of contagious diseases, there is an ongoing discussion surrounding interventions that strategically target individuals who, due to their social network position, are responsible for more infections than others. However, the practical identification of these individuals using conventional network metrics is considerably challenging due to the lack of required data. A potential remedy to this quandary is the development of easily observable proxy metrics for measuring influential spreading. This study aims to assess the viability of such an approach using the example of contact tracing. Utilizing an empirically calibrated agent-based model, the study investigates the extent to which the efficacy of contact tracing can be enhanced by prioritizing influential spreaders, identified using age and household size as proxy measures. The results reveal that the effectiveness of contact tracing is significantly influenced by whose contacts are traced. When the contacts of those causing the most infections are traced, it can substantially enhance the efficacy of contact tracing, even when they are identified solely based on proxy metrics such as age and household size. For the examined case of the German state of Baden-Württemberg, it appears that middle-aged individuals residing in larger households are responsible for most infections. Therefore, prioritizing contact tracing for this specific demographic group seems to be a robust strategy to improve contact tracing. Overall, the results support the potential of the proposed approach to reduce the overall societal costs of non-pharmaceutical interventions while increasing their impact. Further empirical testing of the approach appears worthwhile.

Abstract

The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist rhetoric. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013–2021). Following the ideational definition of populism, we label moralizing references to “the virtuous people” or “the corrupt elite” as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is “thickened,” we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.

Abstract

In this paper, we investigate how protective effects of intergenerational closure correspond with conflict networks in school classes. Taking a multilevel ecological perspective, we also consider networks' socio-spatial conditions. In a first step, we use ERGMs to analyze the association between parental contact and students' friendship ties, i.e., intergenerational closure (IC). Then, we utilize spatial regressions to analyze direct and moderating effects of a school's neighborhood on conflicts in the 135 German class networks (N~=~3143 student measurements). In accordance with Coleman's theoretical notions, we find consistent negative effects of IC on the (standardized) density of the conflict networks. Moreover, we show that IC's impact is particularly strong in neighborhoods with a relatively high concentration of minorities. The results are in line with our theoretical considerations on multilevel network ecologies and the selective pressure of IC against conflict ties. Practically, our results provide evidence that fostering connections among parents (e.g., by implementing opportunity structures for parents to meet) might help to prevent deviating behavior in schools, especially in neighborhoods with relatively large proportions of ethnic minorities.

Abstract

Policy actors (PAs) like nongovernmental organizations, political parties or governmental institutions strategically communicate on social media to gain attention and thus influence the public agenda. We argue that networks of PAs engaged in the same issues (i.e., a PA’s peer network) are crucial to attracting the interest of a broad audience. Drawing on network theory, we posit that (i) ideological homophily, and (ii) the centrality and embeddedness in a PA’s peer network increase the attention received from all Twitter (now X) users. We investigate these premises by analyzing the European migration discourse on Twitter (2014–2020). The results of our study reveal that the centrality of PAs in their peer networks and ideologically similar relations considerably increase attention from the whole Twittersphere. These findings provide strong evidence that a PA’s role in its organizational peer network on social media governs the attention received in the overall discourse.