Prof. Damian Trilling is a Full Professor at the Vrije Universiteit Amsterdam's Faculty of Social Sciences and Humanities in the Department of Communication, and concurrently holds the same rank at the Network Institute. He also serves as a Medewerker (employee) at the Universiteit van Amsterdam and Universitetet i Bergen since February 2024, reflecting his active interdisciplinary and international collaborations. His research focuses on computational methods applied to communication science, particularly exploring how algorithms and digital platforms shape media exposure, political behavior, and information dissemination. Key areas include analyzing selective exposure in recommender systems, measuring ethnic biases in news content, and studying the agenda-setting role of dark platforms. He emphasizes transparency in news algorithms and the ethical implications of data donation frameworks, often employing machine learning techniques like word embeddings and agent-based modeling to address these topics. Trilling's recent articles (2024-2025) reveal a strong focus on understanding algorithmic impacts across media ecosystems. They highlight concerns about filter bubbles, dark platform dynamics, and the interplay between user behavior and platform design. His work bridges communication theory with computer science, offering practical tools such as the INCA infrastructure for automated content analysis and the 3bij3 framework to study recommender systems' effects on news diversity. He teaches courses like Impact van journalistieke producten , Inleiding Media en Journalistiek , and collaborates internationally, as seen in the Introduction to Machine Learning for Text Analysis with Python at GESIS (Mannheim, Germany). His pedagogical contributions complement his research agenda, aiming to equip students with computational skills for modern communication analysis.
