Aktuelle Publikationen

Postindustrial occupational change has ended an era of unprecedented upward mobility. We examine the political implications of this immense structural shift by introducing the concept of status discordance, which we operationalize as the difference between status expectations formed during childhood and outcomes realized in adulthood. We leverage German household panel data and predictive modeling to provide empirical estimates of status expectations based on childhood circumstances and parental background. The analysis reveals that political dissatisfaction is widespread among voters who fall short of intergenerational status expectations. We show that such dissatisfaction is associated with higher abstention rates, less mainstream party support, and more radical voting. Moreover, we explore variation in status discordance by gender, education, and occupation, which influence the choice between radical left and right parties. Our findings highlight how expectations about opportunities underlie generational voting patterns and shed light on the ongoing breakdown of the postwar political consensus.

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dc.contributor.author: Mergel, Ines

Despite recent attention to the economic and political consequences of automation and technological change for workers, we lack data about concerns and policy preferences about this structural change. We present hypotheses about the relationships among automation risk, subjective concerns about technology, and policy preferences. We distinguish between preferences for compensatory policies versus “protectionist” policies to prevent such technological change. Using original survey data from Spain that captures multiple measures of automation risk, we find that most workers believe that the impact of new technologies in the workplace is positive, but there is a concerned minority. Technological concern varies with objective vulnerability, as workers at higher risk of technological displacement are more likely to negatively view technology. Both correlational and experimental analyses indicate little evidence that workers at risk or technologically concerned are more likely to demand compensation. Instead, workers concerned about technological displacement prefer policies to slow down technological change.

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.

International organizations (IOs) are driven by political-administrative interactions between member states and IO administrations. To model these interactions and understand their outcomes, scholars have predominantly, and almost exclusively, relied on agency theory. Yet, as this paper argues, delegation can also take a form of stewardship, where goal conflict and information asymmetries are low. In stewardship relationships, member states trust the IO administration, which enables softer, more informal exercise of control. Both agency and stewardship relationships are illustrated in a comparative case study of FAO and WFP. As interview data and document analysis show, while FAO exhibits agency, WFP provides an example for stewardship. The findings imply that conventional Principal-Agent assumptions should not be taken as given. Not all IO administrations are self-serving agents. The findings also provide implications on IO control and performance and call for scholarship to redirect its focus on de facto rather than de jure IO characteristics.

Background: Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are lacking. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of the content posted on these platforms.
Objective: This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods: We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multiclass and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (term frequency-inverse document frequency with a linear support vector machine) and 2 state-of-the-art deep learning models (Bidirectional Encoder Representations from Transformers [BERT] and XLNet). The first task classified posts into 6 main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either suicidal ideation and attempts or coping and recovery, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these 5 categories. The second classification task was binary and separated posts in the 11 categories referring to actual suicide from posts in the off-topic category, which use suicide-related terms in another meaning or context.
Results: In both tasks, the performance of the 2 deep learning models was very similar and better than that of the majority or the word frequency classifier. BERT and XLNet reached accuracy scores above 73% on average across the 6 main categories in the test set and F1-scores between 0.69 and 0.85 for all but the suicidal ideation and attempts category (F1=0.55). In the binary classification task, they correctly labeled around 88% of the tweets as about suicide versus off-topic, with BERT achieving F1-scores of 0.93 and 0.74, respectively. These classification performances were similar to human performance in most cases and were comparable with state-of-the-art models on similar tasks.
Conclusions: The achieved performance scores highlight machine learning as a useful tool for media effects research on suicide. The clear advantage of BERT and XLNet suggests that there is crucial information about meaning in the context of words beyond mere word frequencies in tweets about suicide. By making data labeling more efficient, this work has enabled large-scale investigations on harmful and protective associations of social media content with suicide rates and help-seeking behavior.

With this work, we present a publicly available data set of the history of all the references (more than 55 million) ever used in the English Wikipedia until June 2019. We have applied a new method for identifying and monitoring references in Wikipedia, so that for each reference we can provide data about associated actions: creation, modifications, deletions, and reinsertions. The high accuracy of this method and the resulting data set was confirmed via a comprehensive crowdworker labeling campaign. We use the data set to study the temporal evolution of Wikipedia references as well as users’ editing behavior. We find evidence of a mostly productive and continuous effort to improve the quality of references: There is a persistent increase of reference and document identifiers (DOI, PubMedID, PMC, ISBN, ISSN, ArXiv ID) and most of the reference curation work is done by registered humans (not bots or anonymous editors). We conclude that the evolution of Wikipedia references, including the dynamics of the community processes that tend to them, should be leveraged in the design of relevance indexes for altmetrics, and our data set can be pivotal for such an effort.

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spread of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of COVID-19 misinformation and vaccination hesitancy. Our study shows that only half of the online users exposed to rumors might have seen corresponding fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. A key finding of this research is that poorer regions were more susceptible to encountering and believing COVID-19 misinformation; countries with lower gross domestic product (GDP) per capita showed a substantially higher prevalence of misinformation. We discuss implications of our findings to public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also defend that fact-checking platforms should prioritize claims that not only have wide exposure but are also perceived to be believable. Our findings give insights into how to successfully handle risk communication during the initial phase of a future pandemic.

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dc.contributor.author: Mergel, Ines; Ney, Steven

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dc.contributor.author: Bardon, Aurélia

We examine the patterns of medical preprint sharing on Twitter during the early stages of the COVID-19 pandemic. Our analysis demonstrates a stark increase in attention to medical preprints among the general public since the beginning of the pandemic. We also observe a political divide in medical preprint sharing patterns - a finding in line with previous observations regarding the politicisation of COVID-19-related discussions. In addition, we find that the increase in attention to preprints from the members of the general public coincided with the change in the social media-based discourse around preprints.

This article adopts a novel regulatory perspective on the conditions that facilitate and obstruct economic equality between migrants and natives. Regulation scholars have long emphasized that regulatory interventions need to be geared toward the needs of regulatory targets. We contribute to this research by examining the fit between regulatory instruments and targets' human capital skills. We develop a theoretical framework that captures how economic integration regulations (EIRs) influence economic equality by supporting or restricting migrants in the labor market and as entrepreneurs. We argue that EIRs foster economic equality when they are responsive to the professional needs of specific types of regulatory targets (in terms of language and education skills). We apply the framework in the context of OECD countries. A fuzzy‐set qualitative comparative analysis reveals how the specific configurations of EIRs in 26 OECD countries coincide with either high or low economic equality between migrants and natives. Our approach contributes to the conceptual understanding of a pressing regulatory problem: the successful economic integration of migrants.

Stricter regulation of food waste reduction is widely presumed to increase food prices, which could render its implementation politically unfeasible. Here we empirically tested whether specific policy framing, design and feedback could help ensure public support despite potential food price increases. We used survey experiments with 3,329 citizens from a high-income country, Switzerland. A combined framing and conjoint experiment shows that messages emphasizing national or international social norms in favour of reducing food waste (policy framing) can increase public support for more ambitious reduction targets. Also, most citizens support food waste regulation even if this leads to substantial increases in food prices, but only if such policies set stringent reduction targets and are transparently monitored (policy design). Finally, a vignette experiment reveals that voluntary industry initiatives do not crowd out individuals’ support for stricter governmental regulation, but potentially crowd in support if industry initiatives are unambitious (policy feedback).