Social media data and other digital behavioral data (DBD) are important for analyzing social science topics in digital societies and for understanding the evolvement of socio-technical systems. GESIS offers methodological insights on how computational methods support social science research and off-the-shelf tools for mining social entities, enriching data and disclosing social structures. With GESIS Notebooks we also provide an infrastructure for reproducible research and for sharing computational tools in this area. The "Total Error Framework for Digital Traces of Human Behavior on Online Platforms" (TED-On) is our first step in building a comprehensive framework for systematic error detection in the collection, processing, and analysis of digital behavioral data.
GESIS aims at providing a comprehensive framework for systematic error detection in the collection, processing, and analysis of digital behavioral data. With focus on social media data, we developed the Total Error Framework for Digital Traces of Human Behavior on Online Platforms (TED-On).
Our Topic Modelling Portal enables stochastic data analysis for web scientists and computational social scientists. The idea is to explain the fundamental mechanisms and ideas behind topic modelling. We provide instruments to detect latent topics in large text corpora while considering contextual information.
Virtual Research Infrastructure
Explore GESIS Notebooks (beta) – we are building an online
environment for web based large-scale data analysis with software suits for coding languages like R or Python. The infrastructure will include services for application, publication, and archiving.