Aktuelle Publikationen

Refugee men are found to be less deserving of government support than refugee women. However, is this still the case if they engage in economic reciprocal behavior and attitudes? Following theories on gender stereotypes and benevolent sexism, we argue that economic activity is expected less of female than of male refugees and that this translates into gendered perceptions of deservingness of financial support. Analyzing data from a 2016 factorial survey experiment in Germany, we show that male refugees are more likely to get “punished” if unwilling to work. Future studies should thus include gender-related aspects when assessing deservingness perceptions.

Does technological change fuel political disruption? Drawing on fine-grained labor market data from Germany, this paper examines how technological change affects regional electorates. We first show that the well-known decline in manufacturing and routine jobs in regions with higher robot adoption or investment in information and communication technology (ICT) was more than compensated by parallel employment growth in the service sector and cognitive non-routine occupations. This change in the regional composition of the workforce has important political implications: Workers trained for these new sectors typically hold progressive political values and support progressive pro-system parties. Overall, this composition effect dominates the politically perilous direct effect of automation-induced substitution. As a result, technology-adopting regions are unlikely to turn into populist-authoritarian strongholds.

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

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.

Students from a high socioeconomic background show relatively homogeneous, high levels of educational attainment, whereas students with a low socioeconomic origin display a large variability in their educational careers. In this paper, we examine whether the varying degrees of students’ academic self-efficacy can contribute to an explanation of this variation. Focusing on Germany’s highly stratified educational system, we utilized the CILS4EU dataset to analyse the association between the academic self-efficacy of students from low and high socioeconomic backgrounds in 9th grade and their later educational aspirations and transitions. Our results show that students from non-academic families are much more likely to (a) aspire to an Abitur, (b) transition to upper secondary school, and (c) enter tertiary education if they exhibit a high level of academic self-efficacy. In contrast, academic self-efficacy shows no link to the educational aspirations and decisions of students who have at least one parent with an academic certificate.

Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news reports. This study applied machine learning to label large quantities of broadcast (TV and radio) media data according to media recommendations reporting suicide. We manually labeled 2519 English transcripts from 44 broadcast sources in Oregon and Washington, USA, published between April 2019 and March 2020. We conducted a content analysis of media reports regarding content characteristics. We trained a benchmark of machine learning models including a majority classifier, approaches based on word frequency (TF-IDF with a linear SVM) and a deep learning model (BERT). We applied these models to a selection of more simple (e.g., focus on a suicide death), and subsequently to putatively more complex tasks (e.g., determining the main focus of a text from 14 categories). Tf-idf with SVM and BERT were clearly better than the naive majority classifier for all characteristics. In a test dataset not used during model training, F1-scores (i.e., the harmonic mean of precision and recall) ranged from 0.90 for celebrity suicide down to 0.58 for the identification of the main focus of the media item. Model performance depended strongly on the number of training samples available, and much less on assumed difficulty of the classification task. This study demonstrates that machine learning models can achieve very satisfactory results for classifying suicide-related broadcast media content, including multi-class characteristics, as long as enough training samples are available. The developed models enable future large-scale screening and investigations of broadcast media.

A vast literature has examined how perceptions of mobility shape demand for redistribution. These studies generally refer to contemporaneous tax policies demanded by those directly impacted. But social mobility is often measured as changes across generations. To account for these intergenerational effects, our analysis focuses on educational policies. We examine how social mobility at the district level explains legislative support for inclusive education policies. We first develop an electoral competition model where voters are altruistic parents, politicians are office seeking and the future economic status of the children is affected both by the degree of income mobility and by public education policies. We then analyze a newly compiled dataset of roll-call votes on California education legislation matched with electoral district levels of income mobility. In line with the model, our analysis suggests that upward mobility in a district negatively predicts legislative support for redistributive education bills.

To what extent, and under what conditions, have workfare reforms shaped public opinion towards the unemployed? This article unpacks the punitive and enabling dimensions of the workfare turn and examines how changes to the rights and obligations of the unemployed have influenced related policy preferences. To do so, it presents a novel dataset on these reforms across a diverse set of welfare states and investigates potential feedback effects by combining our data with four waves of survey data from Europe and North America. Results suggest that while enabling measures generate more lenient attitudes towards the unemployed, punitive measures have no clear effect on public opinion – but they do accentuate the gap between the preferences of high- and low-income individuals. This leads us to conclude that the trend towards punitive and enabling measures since the 1980s has not broadly undermined solidarity with the unemployed, though it has increased income-based polarization.

This study tests if deaths caused by the COVID-19 epidemic influence levels of electoral coordination. Specifically, it tests if a higher number of epidemic deaths strengthens in-group cohesion such that it narrows the votes towards M + 1 parties; or if it weakens in-group cohesion such that it disperses the votes away from M + 1 parties. The results from a multilevel statistical analysis of close to two-thousand observations from Mexico’s 2021 general election suggest that voters struggle to coordinate around a few common options that they believe can best weather the crisis. Moreover, the results from a subsequent time-series cross-sectional analysis provide some support to the expectation that coordination decreased compared to the previous elections.

Bringing together 150+ scholars and practitioners from 50+ countries, and funded by the European Commission, COST Action LeverAge (https://www.cost.eu/actions/CA22120/) is the first network-building project of its kind in the work and organizational psychology and human resource management (WOP/HRM) aspects of work and aging. Focused on the aging workforce, the Action aims to foster interdisciplinary and multinational scientific excellence and the translation of science to practical and societal impact across 4 years. Based on a research synthesis, we identify five broad research directions for work and aging science including work and organizational practices for a multi-age workforce, successful aging at work, the integration of age-diverse workers and knowledge transfer, aging and technology at work, and career development in later life and retirement. We provide key research questions to guide scientific inquiry along these five research directions alongside best practice recommendations to expand scholarly impact in WOP/HRM.

This article introduces a theoretical model of truth and honesty from a psychological perspective. We examine its application in political discourse and discuss empirical findings distinguishing between conceptions of honesty and their influence on public perception, misinformation dissemination, and the integrity of democracy.