SODAS Data Discussion 2 (Spring 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: Aaron Pope
Title: How TikTok Affects Users' Beliefs
Abstract:
TikTok has evolved from an entertainment platform to a major source of political content, with over half of U.S. users relying on it for news. However, there is limited research on how TikTok shapes political beliefs. In this Data Discussion, I will outline a randomized experiment, planned in collaboration with Pia Deshpande (University of California, Berkeley), that will examine the effects of a 4-week TikTok deactivation on users' political views and knowledge. A unique aspect of our study is the collection of participants’ TikTok watch history data, which will allow us to measure baseline content consumption. In this talk, I’ll provide an overview of the project and share some insights we expect to gain from integrating the collection of users' watch history.
Discussion 2
Presenter: Claudia Acciai
Title: Testing the Prevalence of AI in Science
Abstract:
Advances in Generative Artificial Intelligence (GenAI), have sparked a revolution in science, providing researchers with unprecedented computational power and opening new prospects for progress. While scientists have used AI for decades, the rapid advancement of GenAI has significantly accelerated its adoption. Yet, little is known about the extent to which scientists are actually using these models, the factors influencing their adoption, and whether self-reporting GenAI use is perceived as a threat to self-image and professional representation. This ambiguity raises important questions about what is considered socially acceptable within scientific communities.
When norms are unclear, as with the emerging use of GenAI, researchers may be unsure about what constitutes appropriate usage. This uncertainty can contribute to social desirability bias, where scientists adjust their reported use to align with perceived professional or ethical expectations. Consequently, traditional survey methods may fail to capture the true extent of GenAI adoption, potentially overstating, or understating, its usage.
To address these challenges, we are conducting a survey with three distinct panels of scientists, utilizing advanced techniques – including list experiments and vignettes – to enhance the reliability of self-reported data. By examining how social norms and perceptions of acceptability influence reported use, our study aims to quantify the prevalence of GenAI in science and shed light on the social dynamics shaping its acceptance.