Philipp Knöpfle’s dissertation explores replicability in communication science. Using AI and statistical analyses, it identifies methodological barriers and develops a framework for more transparent and robust replication practices.
My dissertation examines factors influencing replicability in communication science and aims to develop a deeper understanding of the challenges and opportunities associated with digital research data. Using methods such as machine learning, natural language processing, and statistical analyses, I identify key barriers, including data and code availability, context dependencies, and the dynamics of volatile data scenarios like social media.
The project combines theoretical analysis with empirical validation to develop a conceptual framework that systematically organizes the structural and methodological determinants of replicability. This framework seeks to lay the groundwork for more transparent and robust replication practices. My objective is to contribute to scholarly discussions on replicability in communication science and to provide practical insights for advancing methodological approaches.
Meta Science | Computational Communication Science | Replicability
Academic Staff
Open Science • Meta Science • Computational Methods • AI & LLMs
The project includes several research projects within LMU. That's because it's great and very interdisciplinary.