Felicia combines a strong communication science background with very good technical skills. She completed the UvA research master in Communication Science. For her thesis she implemented her own news recommender system and web interface in python for conducting live, large-scale experiments tracking usage over time. It combines web scraping, machine learning, different customization options as well as gamification elements to enable testing various forms of news personalization in a controlled, yet realistic environment.
In the filter bubbles project, she will concentrate on the substantive analysis of (mobile) digital traces of news consumption behaviour: what news are people actually exposed to and choose to consume, can we find substantial evidence for echo chambers or filter bubbles, and if so what are the possible consequences for political knowledge and attitudes? Her other research interests include the interplay between political public relations and (social) media as well as furthering the usage of computational methods in communication science. She contributes for example to the open-source project INCA to facilitate collection, processing and analysis of online (textual) data for social scientists.