We are the CSS department at GESIS and our mission is to support scientists who aim to work with digital behavioral data to learn about social and socio-technical phenomena. We support scientists in the different phases of their research ranging from the planning and design of a study to the data collection, the data analysis and the documentation. We follow open science principles and help other CSS researchers to comply with these principles.
This account contains open source material created by our members. The Team "Transparent Social Analytics" also has a GitHub account where they share open source material. More open resources can also be found in the GitHub repositories of some of our members:
Arnim Bleier, Chung-hong Chan, Julian Kohne, David Schoch
We also maintain an awesome list for computational social science
Rehydrate Btw17 Deleted Tweets
Social Media Monitor Wrapper
Social Media Monitor Demo
WebBot: Scrape search engine results
Parsing WebBot results with Python (R version)
Basic analysis of the sociopatterns data sets
WikiWho Api
WikiWho Interactions
WikiWho Wrapper
WikiWho Tutorial
WikiWho Demo
WikiWho Pickle
WikiWho Chobj
WikiWho Tsne
Parse Page Views
Wikipedia References
Interactive Wikipedia Article Analysis Notebooks
WikiSenti
Gender Inference
Improve Gender Identifier
Unsexistifyit
Stance Detection
Detect Media Frames
Sexism Custom Classifier
Theory Driven Sexism Detection
Practical Introduction to Text Mining
Topic- and Structured Topic Modeling
Introduction on how to use spaCy for NLP projects
Example usage of PolmineR
Introduction to Networkx
Introduction to Social Network Analysis
Introduction to Graph-Tool
Data-driven discrimination in relational classification
Hoprank
Homophilic Network Minorities
Homophilic Networks
Homophilic Directed Scalefree Networks
Introduction to Quarto 2023
Material for Computational Sociology
Methods Seminar 2019
Gesis Dataday 20
Youtube Workshop
Methods Seminar 2020 Network Science
Open Research Computing Getting Started with GESIS Notebooks
