Brief summary of research

In my research, I leverage interdisciplinary insights to explore the conditions under which news recommenders/websites can facilitate democratically important outcomes such as political learning or tolerance. As such, my research contributes to a growing body of work that critically examines the effects of digital platforms and explores potential strategies for redesigning them.

To this end, my research explores the drivers of news selection in digital environments, the effects of user interface design and algorithmic ranking on news selection, and the effects of news consumption on political learning, political efficacy and tolerance. Some of my side interests include nudging, explainability, algorithmic literacy, selective exposure, news diversity and computational social science more generally.

I am a strong proponent of open science principles and interdisciplinary science. Methodologically, I predominantly use experimental approaches (lab & field experiments), at times combined with qualitative methods such as interviews and think-aloud protocols. I also have a keen interest in computational social science and prior experience working with automated text analysis, web-scraping as well as frontend and backend development (using React and Python respectively).