Research Areas
Media Effects, Opinion Formation, Partisanship
The Impact of Media Framing in Complex Information Environments
Conditionally Accepted, Political Communication
Abstract
To what extent do news frames influence public opinion? While a large body of experimental research suggests sizable effects, it is unclear how these findings translate to authentically complex information environments. I exploit a rare change in the immigration framing of the largest German tabloid, Bild, to estimate the precise impact of this shift on immigration attitudes using large-scale panel data. Despite a 42% increase in the emphasis on crime in Bild’s immigration coverage, I find a robust and precise null effect on immigration attitudes and several related variables. These findings highlight that framing effects materialize under specific scope conditions that need to be considered when generalizing from experimental results.Manuscript Slides Replication Materials
The Effect of News Framing on Voting Intentions
Revise and Resubmit
Abstract
Framing is a common strategy in political communication and a large body of research documents the substantial effects of this rhetorical device on political attitudes. However, opinion manipulation often represents merely an intermediate goal in a greater effort to affect citizens’ primary instrument of political influence: voting. From a rational model of voting, it follows that framing should have downstream consequences for voting intentions. In this manuscript, I outline a theory of when framing effects on policy attitudes should translate into voting intentions, drawing on rational choice as well as identity-based theories of voting behavior. I hypothesize that citizens should resolve cognitive dissonance emerging from an increased or decreased distance to a party’s issue position by either updating their inclination to vote for the party or by changing their issue position. Partisans should adapt their issue attitude in support of their party, whereas non-partisans should update their voting intentions to reflect the party’s issue distance.
I test these pre-registered hypotheses in a representative survey experiment fielded in Germany (N = 3004). The results suggest that while citizens’ attitudes are responsive to framing, this responsiveness does not translate into voting intentions. Citizens do adjust their attitudes to support their preferred party when learning about parties’ policy positions. However, attitudinal changes do not translate into voting intentions, irrespective of respondents’ attachment to a given party. These results underline the stability of voting intentions, even in the context of attitudinal change, and suggest that cue-taking is a more conscious process than is often theorized. Most importantly, my results contradict a mechanistic understanding of the connection between policy preferences and voting intentions.
Manuscript Slides Pre-Registration
Framing and Voting - The German Immigration Debate and the Effects of News Coverage on Political Preferences
Dissertation, Humboldt-Universität zu Berlin
Abstract
A large experimental literature on framing effects suggests that citizens form rather limited political preferences, open to severe manipulation. If citizens’ attitudes were always so easily malleable for media outlets and political actors, it would not constitute a very meaningful input for the democratic process. This dissertation asks how these experimental findings translate into complex, real-world news environments and whether news frames structure citizens’ voting intentions. It provides a clear conceptualization of frames, on which it builds a method to identify news frames automatically, and theorises a link between news frames and voting intentions. The dissertation presents original and secondary data, exploring the relationship of news framing, immigration attitudes, and voting intentions. Providing a broad overview of immigration framing in the German news media, it shows that neither immigration attention nor framing can explain the rise of the radical-right AfD. It then exploits a change in the immigration framing of Germany’s largest tabloid, Bild, showing that this shift had no effects on immigration attitudes or voting intentions among its readers. The final empirical chapter presents experimental evidence revealing that framing only affects voting intentions among rather uninformed citizens. The findings contribute to the study of framing and public opinion, suggesting that citizens’ attitudes are not as easily manipulated and the power of the news media more limited than often thought. Instead, framing effects take place under highly specific conditions, which are often not fulfilled. The emerging picture of public opinion is one of crystallized and resistant attitudes, which only respond to novel events. In other words: whoever gets to the voter first, wins. Politics, in this view, is a pattern of critical events following upon each other, each presenting a unique opportunity to change the dominant understanding of an issue.
Realignment on High-Stakes Issues: Evidence from Local Vaccination Behavior in Germany
Ongoing project
Abstract
A large literature debates the extent to which citizens’ issue attitudes follow or lead parties’ policy positions. I argue that current research underemphasizes the impact of attitudes on voting intentions by mostly focusing on weakly held, variable issue attitudes. This issue is often exacerbated by the lack of attitudinal data preceding the politicization of political Issues. This study draws on data of local vaccination behavior from Germany to address this issue. I study whether parties’ positional shifts during Covid led individuals to change their vaccination behavior or adapt their party preferences. I show that changes in local party support are strongly correlated with historical vaccination rates, but only for the election following the onset of the Covid pandemic. In addition, I present evidence from an event study exploiting a local leaders’ public declaration not to get vaccinated. I find limited effects of this partisan cue on local Covid vaccinations. These results suggest that realignment is the norm rather than the exception on high-stakes issues.
Countering Democratic Backsliding
Improving the Recognition of Democratic Norm Violations
Survey experiment aiming to improve citizens’ recognition of democratic norm violations. Joint work with Markus Kollberg.
Abstract
While most voters claim to support democratic norms, they still vote for parties and politicians violating these norms. Previous work has argued that this is the result of partisans selectively tolerating violations. We argue that voters are often not familiar with key democratic norms, and therefore unable to accurately identify norm violations. In absence of this knowlege, they rely on partisan cues. We run a survey experiment using a two-wave panel survey in Germany to put this theory to the test. In the first wave, we inform voters about democratic norms and backsliding using a video and a quiz. In the second wave, we assess their sensitivity to democratic norm violations using a conjoint experiment, evaluations of two real-world norm violations, and a hypothetical scenario. Our results will reveal whether making citizens aware of key democratic norms and their potential violation enables them to accurately recognize violations, assign blame, and adjust their voting intentions to defend democracies. These findings can inform future education campaigns about democratic norms to prevent support for illiberal actors even in the face of substantial partisan polarization.
Online Discourse
What is a Good Conversation? Improving the Operationalization and Measurement of Deliberative Quality by Analyzing Interactions
Ongoing project developing classifiers for the measurement of deliberative quality in online discourse. Together with Paco Tomas-Valiente, Lena Song, Dominik Stammbach, Laura Bronner, and Elliott Ash
Abstract
Deliberation - the exchange of arguments to inform a collective understanding of an issue - is a key component of opinion formation and, therefore, democratic decision-making. Theorists of deliberation conceptualize deliberative quality as a property of entire communicative interactions, involving respect, justification, and reciprocal engagement. However, prior attempts at operationalization - both in the interpretive and computer science traditions - have treated each debate contribution, like a speech or online comment, as a single unit of analysis, ignoring the interactive nature of this process and failing to measure the deliberative quality of the entire communicative exchange. In this paper, we empirically show that operationalizing deliberation at the interaction level captures important aspects of the concept which cannot be measured when treating single debate contributions as distinct entities. Using data from three different online outlets, we show that large language models (LLMs) can achieve performance comparable to human annotators when evaluating deliberative quality in online conversations. Furthermore, we show that LLMs better identify the deliberative quality of an interaction, especially with regards to its reciprocity and respectfulness, when shown entire conversations, rather than individual comments separately. Based on a synthetic-labeling approach, we train and publish adapter modules, providing an off-the-shelf tool to researchers interested in the measurement of deliberative quality and its underlying dimensions.
Tackling harmful online comments with visible moderator presence
Ongoing project with Laura Bronner, Paco Tomas-Valiente, and Dominik Hangartner.
Abstract
Online platforms often respond to harmful content by deleting or downranking it, but moderation actions are generally invisible to most users. Since moderation is typically invisible, online discussions feel like spaces where no one is enforcing conversational norms, which can lead users to produce toxic messages or fail to engage with other arguments. Can the visible engagement of clearly identifiable moderators in comment sections - what we call ‘active moderation’ - improve online discourse? Together with the Swiss online newspaper 20Minuten, we run a randomized field experiment testing the efficacy of such visible moderation. We use an experimental design in which 20Minuten moderators identify articles they deem at risk for harmful comments and randomize them into two categories – one control group, which experiences the usual invisible moderation (deleting harmful comments before publication), and one treatment group, in which moderators additionally engage visibly in the comments section, responding to both constructive and potentially harmful comments. We find that active moderation reduces both the number and the share of toxic comments submitted; moreover, it does so without reducing engagement. We show that this reduction in toxicity is entirely due to behavioral change among users, rather than selection of less toxic users into actively moderated articles.
Does preventive content moderation improve online debates?
Ongoing project with Laura Bronner, Paco Tomas-Valiente, and Dominik Hangartner.
Abstract
Many online platforms use forms of pre-moderation, whereby online contributions are screened - by algorithms, humans or both - with the aim of identifying harmful comments before they are posted. This system has disadvantages: it can be costly, depending on the cost of the human (and/or algorithmic) moderators used, and it can inhibit on-platform interaction by delaying or even preventing the posting of comments, including harmless ones. But does it actually work to reduce harmful comments and improve the quality of online discourse? And what is the trade-off between harmful comments and the costs (financial and otherwise) to the platform?
In this study, we partner with the Austrian online newspaper Der Standard to run a randomized field experiment testing the efficacy of algorithmically-supported pre-moderation. In our experimental design, the Community Team at Der Standard randomizes articles into two categories: one treatment group, in which comments are passed through their in-house moderation algorithm, which automatically publishes or deletes most comments and sends the remainder to human moderators, and a control group, in which comments are not systematically pre-screened. We track how submitted and published comments differ between treated and control articles to measure the impact of pre-moderation. We hypothesize that there is a trade-off between preventing harmful speech and encouraging engagement; we expect that pre-moderation reduces harmful speech, but also takes longer, leading to fewer comments and interactions, and requires more resources from the platform.
Text Analysis/NLP
Measuring Rhetorical Similarity with Supervised Machine Learning
Abstract
Recent advances in the application of supervised learning have shown how the method can be employed to measure polarization by assessing classifiers’ accuracy. Building on these contributions, I propose a reconceptualisation to enable extended utilization of this approach. Focusing on predicted probabilites as a measure of rhetorical similarity, I validate supervised learning for the measurement of accommodation to radical right parties through established parties and politicians in Austria, Germany, and the Netherlands. Results indicate that the method produces valid estimates of parties’ and speakers’ rhetorical similarities to respective radical-right parties and outperforms existing similarity measures and scaling methods. I discuss possible further applications and limitations.
Measuring Media Bias with Cross-Domain Deep Learning
Work together with Tom Arend, looking into cross-domain learning to estimate newspapers’ leanings towards different political parties.
Abstract
The measurement of ideology is one of the major applications of text analysis in political science. However, researchers often face scarcity of available labelled data totrain supervised models for their specific domain. Manualannotation is costly and often severely affected by subjective bias. We propose the use of cross-domain learning to fine-tune transformer models on available, labelled political texts issued by political parties to obtain a classifierof political ideology. Using a unique dataset of newspaperarticles authored by politicians, we test such an application in the German context. Comparing transformer neural networks fine-tuned on different data sets of party pressreleases, we present evidence on the feasibility of such anapproach. This contributes to the broad literature on textanalysis in the political domain, informing researchers onthe limitations of training powerful deep learning modelson political language with scarce training data. The experiments conducted by the authors indicate that the trainingon party labels cannot be easily translated to the domainof newspaper articles but that supervised models are highlysensitive to minor changes in the inputs. It is thus recommended to refrain from using deep learning in the absenceof sufficient training data.
Political Scandals
Die Nationalratswahl 2017 unter besonderer Berücksichtigung der Silberstein-Affäre
Chapter in: Weßels, B., & Schoen, H. (2021). Wahlen und Wähler. Analysen aus Anlass der Bundestagswahl 2017. Springer VS Wiesbaden. Co-authored with Julia Partheymüller and Jakob-Moritz Eberl
Silberstein und Kern - hat der Skandal der SPÖ geschadet?
Blog-post (Coauthored with Markus Wagner) on a scandal in the Austrian Lower House Election 2017; also covered by Austrian daily newspaper “Der Standard”.