Working Papers

The Impact of Media Framing in Complex Information Environments

Conditionally Accepted, Political Communication

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.

Working Paper | Github | Slides

The Effect of News Framing on Voting Intentions

Revise and Resubmit

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.

Working Paper | Github | Slides


Work in progress

Improving the Recognition of Democratic Norm Violations

Survey experiment aiming to improve citizens’ recognition of democratic norm violations, coauthored with Markus Kollberg.

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.

What is a Good Conversation? Improving the Operationalization and Measurement of Deliberative Quality by Analyzing Interactions

Joint project with Francisco Tomás-Valiente, Lena Song, Dominik Stammbach, Laura Bronner, and Elliott Ash

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.

Leading by Example? How Elite Behaviour affects Individual Vaccination Decisions

Ongoing project assessing the impact of party cues on vaccination behaviour

Github | Slides

The Impact of Emphasis Framing on Party Competition

Dissertation project combining transformer models and document embeddings to assess the impact of news framing of migration on political parties’ electoral support

Github

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.

Report | Blogpost | GitHub

Right-Wing Terrorist Attacks, the Media’s Reactions, and Radical Right Party Support

Ongoing project (with Werner Krause) studying media reactions to radical-right terrorism in Germany

Gitlab


Publications

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

Publication


Teaching

Supervised Machine Learning with Imbalanced Data

Self-designed workshop introducing different techniques to deal withimbalanced data in supervised classification problems.

Github | Slides

CompText 2022 & 2023

Methods II: Quantitative Methods

Tutorial introducing Master’s students to fundamental concepts of quantitative methods, such as experiments, regression, causality, and hypothesis testing, and their application in R.

Fall 2023 and 2024, ETH Zurich

Intro to R and Statistics

Tutorial for Master students, focusing on the R programming language.

Fall 2020 and 2021 (two labs each term), Hertie School, Berlin


Academic Blogposts

Classifying Newspaper Bias with Cross-Domain Learning

Can we use party communication to train transformer models detecting newspaper bias? The short answer: no.

Blogpost | Report | GitHub

May 2021

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”.

Blogpost | News article

March 2018


Data

Word embeddings of German Newspaper articles

Word embedding model of over 2.2M German newspaper articles 2013-2021 (Bild, Frankfurter Allgemeine, Spiegel Online, Süddeutsche Zeitung, TAZ, Welt).

Dataset | Github

August 2021, HU Berlin, Germany

EUSpeech v2.0. A dataset of 11,466 speeches by European Leaders

In charge of data collection using web-scrapers

Dataset | Report

September 2018 – July 2020, University of Amsterdam, Netherlands

AUTNES Online Panel Study 2017

Responsible for data cleaning, manual coding and preparation of questionnaires.

Dataset

April 2017 - March 2018, University of Vienna, Austria

AUTNES Comparative Study of Electoral Systems Post-Election Survey 2017

Responsible for data cleaning, manual coding and preparation of questionnaires.

Dataset

April 2017 - March 2018, University of Vienna, Austria


Dormant

Apart - Affective Polarization in Text

Collaborative project (with João Areal Neto and Phillip Mendoza) measuring hostility in parliaments using targeted sentiment analysis

Github

September 2019 – February 2020