Aktuelle Publikationen

News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage.

This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles.

Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.

The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who ‘speak their mind’ are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence. By analysing communications by members of the US Congress on Twitter between 2011 and 2022, we show that politicians’ conception of honesty has undergone a distinct shift, with authentic belief speaking that may be decoupled from evidence becoming more prominent and more differentiated from explicitly evidence-based fact speaking. We show that for Republicans—but not Democrats—an increase in belief speaking of 10% is associated with a decrease of 12.8 points of quality (NewsGuard scoring system) in the sources shared in a tweet. In contrast, an increase in fact-speaking language is associated with an increase in quality of sources for both parties. Our study is observational and cannot support causal inferences. However, our results are consistent with the hypothesis that the current dissemination of misinformation in political discourse is linked to an alternative understanding of truth and honesty that emphasizes invocation of subjective belief at the expense of reliance on evidence.

Background: Although the problem of obstetric violence (OV) is receiving increasing attention among academics and policy makers, the prevalence and associated factors of OV are still poorly understood. The fear of OV prevents women from giving birth in health facilities, which is crucial for the effectiveness of HIV prevention programs, such as the Prevention of Mother to Child Transmission (PMTCT) program.

Objective: The aim of this paper is to determine the prevalence of OV, the comparative burden and associated predictors between HIV positive and negative women in Ghana.

Methods: The present study is a facility-based cross-sectional study enrolling 2,142 women, of which 310 were HIV positive and 1,832 HIV negative with a birth history of 0-24 months. The women were enrolled consecutively using a two stage sampling technique from eight hospitals with antiretroviral clinics across two regions in Ghana. The primary outcome variable was the experience of OV and its various forms. Descriptive data is presented in tables and reported in frequencies. The inferential analysis has been performed by estimating the Adjusted Odds Ratios (AOR) using multivariate logistic regression.

Results: Prevalence of OV was slightly lower among HIV positive women (61.0%) compared to HIV negative women (65.1%), though this was not statistically significant (χ2=1.99; p=0.158). The most common sub-category of OV experienced by all women was non-confidential care (35.2%). HIV positive women experienced more abandoned care (32.6%) with non-consented care being the least prevalent form of OV. Experience of discrimination was higher among HIV positive women (13.5%) than HIV negative women (10.8%). The multivariate regression analysis of the predictors of OV suggests that HIV positive women are not more likely to experience OV. Instead, we find evidence that HIV positive women are less likely to be subjected to physical violence (AOR=0.512; CI: 0.369-0.710), non-consented care (AOR=0.457; CI: 0.244-0.859) and non-dignified care (AOR= 0.688; CI: 0.513-0.923).

Conclusions: The study shows high rates of OV among all women. However, we found no evidence that HIV positive women were at higher risk to experience OV than HIV negative women. Evidence based interventions are required to address OV due to its threat to facility-based childbirth and the PMTCT cascade of care.

Goodness of fit tests for two probabilistic multigraph models are presented. The first model is random stub matching given fixed degrees (RSM) so that edge assignments to vertex pair sites are dependent, and the second is independent edge assignments (IEA) according to a common probability distribution. Tests are performed using goodness of fit measures between the edge multiplicity sequence of an observed multigraph, and the expected one according to a simple or composite hypothesis. Test statistics of Pearson type and of likelihood ratio type are used, and the expected values of the Pearson statistic under the different models are derived. Test performances based on simulations indicate that even for small number of edges, the null distributions of both statistics are well approximated by their asymptotic χ2-distribution. The non-null distributions of the test statistics can be well approximated by proposed adjusted χ2-distributions used for power approximations. The influence of RSM on both test statistics is substantial for small number of edges and implies a shift of their distributions towards smaller values compared to what holds true for the null distributions under IEA. Two applications on social networks are included to illustrate how the tests can guide in the analysis of social structure.

Since the Eurozone crisis, intense political debate has resurfaced about deservingness judgements in European solidarity. To contribute to this debate, this article proposes a refined concept of ‘multi-level blame attribution’. It postulates that public support for EU-level welfare policies crucially depends on how citizens attribute responsibility for economic outcomes across different levels of agency. Results from an original public opinion survey conducted in 10 European Union member states demonstrate that attributing blame to individuals decreases citizens’ willingness to show solidarity with needy Europeans, whereas attributing blame to the EU increases support. The role of attributing blame to national governments is dependent on the country context; beliefs that worse economic outcomes are caused by national governments’ policy decisions tend to dampen support for EU targeted welfare policies only in the Nordic welfare states. The article concludes by discussing the implications of multi-level blame attribution for the formation of public attitudes towards European solidarity.

Agenda-setting focuses on how political issues emerge within society, enter parliamentary debates and are responded to by government decisions. We introduce a database that traces policy issues in Germany between 1978 and 2017. These political activities include political inputs (public opinion), processes (party manifestos, parliamentary questions and government speeches), as well as outputs (laws). Each activity’s topic is identified using the Comparative Policy Agendas scheme. Collectively, these observations comprise the policy agendas of Germany. We highlight the database potential by describing all German policy issues for the 39-year period and by tracking how immigration became a political issue.

Governmental measures against the spread of the Corona virus have been met with varying levels of opposition in many countries worldwide. Existing research has claimed that some of this opposition is linked to esoteric and anthroposophical beliefs. This research note tests this in an observational study using election results from the 2021 parliamentary election in Germany and new data on the distribution of natural healers, homeopathic doctors and Steiner schools. Results show that counter to common expectations, there is no evidence that esoteric beliefs systematically lead to increased support for the established right-wing AFD. Rather, some indicators for esoteric beliefs – in particular, the presence of homeopathic doctors and Waldorf

schools – are related to higher support for the new fringe party dieBasis, a single-issue party campaigning against governmental Corona measures.

Collective, citizen-led approaches to energy systems are key to reducing humanity's climate impact. Crucial for their success is the engagement of energy prosumers, i.e., citizens that produce energy. This paper investigates how to best activate the identities of prosumers to participate in peer-to-peer energy trading. In collaboration with the OurPower cooperative in Austria, an experimental trial was set up: 8713 households equipped with photovoltaic systems were randomly selected to receive one of three postcards. Postcards appealed to different identities, using as slogan “your climate. your electricity” or “your friends. your electricity” or “your region. your electricity”. The postcards were designed to encourage prosumers to visit the OurPower webpage and register via a link and QR code to trade their energy. For our main dependent variable, the unique webpage visits, we recorded a 3.1 % response rate (N = 271 visitors out of 8713 recipients) and found that while the social identity framings (region and peer-group) did not significantly differ in response rates, they both outperformed the individual climate identity framing. For click-through to express more interest (N = 73 out of 271 visitors), the region framing was more successful than the peer-group framing, but not the climate framing. For length of stay on the website (N = 145 visitors), regional framing (M_r = 73.5 s) held an advantage over the other two (M_climate = 22.9 s; M_friends = 17.5 s). Together, the results demonstrate that putting regional focus into the spotlight could be a promising consideration for future energy-related campaigns and interventions.

How do task groups react to poor performers? We integrate attribution theory with individual motivation theories in a novel, parsimonious model that makes nuanced predictions. Our model asserts that group members assess the poor performer's intent to help the group (i.e., pro-group intent) by first considering the poor performer's characteristics suggested by attribution theory: effort and ability. While attribution theorists have mainly assumed that low effort reflects lacking desire to contribute to group goals and that it is infeasible to acquire ability, motivation theories assume individuals set their goals to perform tasks and acquire skills based on both desirability (value) and feasibility (expectancy). As group members may well assume that a poor performer uses these criteria when forming a pro-group intent to contribute to group goals, low effort may also reflect the infeasibility of making the required contributions, and low ability may reflect a low desire to acquire new skills. Therefore, our model of pro-group intent predicts that desirability-feasibility assumptions moderate the effort-ability effect on reactions to poor performers and that evaluations of pro-group intent mediate this effect. Indeed, in five experiments (total N = 1,011), low effort only produced more negative reactions than low ability when a desirability attribution was made for effort, and a feasibility attribution was made for ability. In contrast, reversing these assumptions eliminated the effort-ability effect. This interaction was fully mediated by the performer's perceived pro-group intent. We discuss how our (meta-) intentional perspective informs existing accounts of poor performers, group processes, and motivation science. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This study is motivated by the distinctive outcome of the minority achievement gap in Estonia and Latvia, countries with similar legacies and socio-economic development. We have four sub-groups of schools involving pairs of instructing languages: Estonian and Russian in Estonia, and Latvian and Russian in Latvia. All four are above average performers according to international comparisons. Still, our data show that a remarkable achievement gap between majority and minority students exists only in Estonia. We employ the Oaxaca–Blinder twofold decomposition technique to explore the factors behind the minority achievement gap (MAG). We are able to explain almost half of the gap in Estonia by peer effects and the larger concentration of immigrants in minority schools. In Latvia, on the contrary, the average peer effect is positive in minority schools. Still, regarding the essence of the unexplained gap, our results remain inconclusive.

Why do legislators engage in geographic representation in party-centred electoral systems, where they face weak re-election incentives to cultivate a personal vote? Existing research offers cross-pressuring incentive structures and intrinsic localism motivations as individual-level factors to explain this puzzle. In this article, we propose an alternative argument based on the principle of collective action within party-internal structures of labour division. We argue that legislators elected in the same multi-member district and under the same party label (party delegations) share collective vote-seeking incentives to collaborate with each other in order to strike a balance between the collective benefits and individual costs of constituency-oriented activities. Results from a comparative study of written parliamentary questions in Germany and Spain support our argument. Specifically, the study suggests that individual localism attributes interact with the team composition of party delegations to shape constituency-orientated behaviour.

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Sanctions are among the most frequently used foreign policy tools to address human rights violations, but they can be highly politicized. Since the early 2000s, human rights sanctions have been increasingly triggered by standardized rankings of states’ performances. While research on economic statecraft suggests that coercive measures based on cross-national assessments may be less influenced by strategic considerations, scholarship on rankings highlights how standardized performance indicators can also be political. This paper investigates whether sanctions based on standardized human rights assessments are also influenced by senders’ strategic political and economic interests. Empirically, we examine the case of United States human trafficking sanctions that combines universal rankings in the first stage and country-specific sanctions waivers in the second. The analysis leverages novel data on all Trafficking in Persons (TIP) rankings by the US State Department and presidential sanctions waivers from 2003 to 2018. Despite the TIP report's reputation as a reliable indicator, we find that both stages in the process of imposing human trafficking sanctions are driven by strategic attempts to minimize the economic and political costs of sanctions for the US. These findings have broader implications for the reputation and effectiveness of other human rights rankings by the US.