Social scientists are increasingly drawing on new forms of data beyond the traditional genres of surveys and interviews. These data include administrative records, commercial transactions, social media and internet data, geo-spatial data, and image and audio data. These types of new data present challenges for technical infrastructure, legal governance, and ethically responsible research and digital preservation.
Researchers at GESIS are investigating several vital topics in this domain:
- linking digital behavioral data (e.g., social media) with survey data
- digital preservation of social media data to enable access while protecting privacy
- georeferencing surveys to link them to small-scale neighborhood information found in geo-spatial data
Learn more about our consulting and services:
Analyzing Digital Behavioral Data
Methods, tools, frameworks and infrastructures for analyzing digital behavioral data.
CSS Capacity Building
Talks, tutorials, materials on computational methods for the collection, processing, and analysis of digital behavioral data.
- Conference on Harmful Online Communication (CHOC2023)
Digital Behavioral Data: Datasets
Curated digital behavioral data – datasets for scientific re-use.
- Zagovora, Olga, Roberto Ulloa, Katrin Weller, and Fabian Flöck. 2022. ""I updated the ": The evolution of references in the English wikipedia and the implications for altmetrics." Quantitative Science Studies 3 (1): 147-173. doi: https://doi.org/10.1162/qss_a_00171.
- Bittermann, André, Veronika Batzdorfer, Sarah Marie Müller, and Holger Steinmetz. 2021. "Mining Twitter to detect hotspots in psychology." Zeitschrift für Psychologie 229 (1): 3-14. doi: https://doi.org/10.1027/2151-2604/a000437.
- Sen, Indira, Fabian Flöck, Katrin Weller, Bernd Weiß, and Claudia Wagner. 2022. "Applying a total error framework for digital traces to social media research." In Handbook of Computational Social Science. Volume 2: Data science, statistical modelling, and machine learning methods, edited by Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, and Lars Lyberg, 127-139. Routledge.
- Schaible, Johann, Marcos Oliveira, Maria Zens, and Mathieu Génois. 2022. "Sensing Close-Range Proximity for Studying Face-to-Face Interaction." 1. In Handbook of Computational Social Science; Vol 1: Theory, Case Studies and Ethics, edited by Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, and Lars Lyberg, European Association of Methodology series, 219-239. Abingdon, Oxon: Routledge.
- Sen, Indira, Fabian Flöck, Katrin Weller, Bernd Weiß, and Claudia Wagner. 2021. "A Total Error Framework for Digital Traces of Human Behavior on Online Platforms." Public Opinion Quarterly 85 (S1): 399–422. doi: https://doi.org/10.1093/poq/nfab018.