SODAS Data Discussion 3 (Fall 2025)
Copenhagen Center for Social Data Science (SODAS) aspirers to be a resource for all students and researchers at the Faculty of Social Sciences. We therefore invite researchers across the faculty to present ongoing research projects, project applications or just a loose idea that relates to the subject of social data science.
Two researchers will present their work. The rules are simple: Short research presentations of ten minutes are followed by twenty minutes of debate. No papers will be circulated beforehand, and the presentations cannot be longer than five slides.
Discussion 1
Presenter: Daniel Juhász Vigild
Title: How Government Use of AI Impacts its Trustworthiness
Abstract:
Artificial intelligence has attracted considerable interest as a means of improving efficiency and productivity in the public sector and enhancing the quality of public services. Yet, deploying AI in public institutions may also create unintended social costs that undermine these potential benefits. Across two survey experiments (N = 2,000) examining the use of AI in four different public institutions, we assess how AI adoption affects perceptions of institutional trustworthiness. We find that AI reduces trust in some, but not all, of the institutions in our study. To account for these divergent effects, we propose "algorithmic nimbyism" as a mechanism that helps explain variation in how AI impacts institutional trust.
Discussion 2
Presenter: Stephanie Brandl
Title: Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump's Presidential Campaigns
Abstract:
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of instruction-following tasks, yet their grasp of nuanced social science concepts remains underexplored. This paper examines whether LLMs can identify and classify fine-grained forms of populism, a complex and contested concept in both academic and media debates. To this end, we curate and release novel datasets specifically designed to capture populist discourse. We evaluate a range of pre-trained (large) language models, both open-weight and proprietary, across multiple prompting paradigms. Our analysis reveals notable variation in performance, highlighting the limitations of LLMs in detecting populist discourse. We find that a fine-tuned RoBERTa classifier vastly outperforms all new-era instruction-tuned LLMs, unless fine-tuned. Additionally, we apply our best-performing model to analyze campaign speeches by Donald Trump, extracting valuable insights into his strategic use of populist rhetoric. Finally, we assess the generalizability of these models by benchmarking them on campaign speeches by European politicians, offering a lens into cross-context transferability in political discourse analysis. In this setting, we find that instruction-tuned LLMs exhibit greater robustness on out-of-domain data.