Analyzing Digital Behavioral Data
TED-On
Methodology, Framework
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).
Paper | Extended Paper | MTE Talk | Slides | Tutorial@FAT | TES-D Paper
Topic Modelling
Portal
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.
GESIS Notebooks
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.
WikiWho
API, Tool
Use the WikiWho Tool for 'social' text mining and analyze editing and revising transactions of Wikipedia entries across languages. Data can be downloaded as data set or obtained via an API.
WikiWho API | Data | WikiWho Wrapper | Report | Tutorial | Paper | WhoColor Documentation | Paper
