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New forms of data

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:

  1. linking digital behavioral data (e.g., social media) with survey data
  2. digital preservation of social media data to enable access while protecting privacy
  3. georeferencing surveys to link them to small-scale neighborhood information found in geo-spatial data

Learn more about our consulting and services:

  • Batzdorfer, Veronika. 2022. "Theory-driven modelling of complex socio-psychological constructs in text." Invited Panel Talk on the Workshop on Computational Linguistics for Political Text Analysis (CPSS-2022), Universität Potsdam, 2022-09-12.
  • Batzdorfer, Veronika. 2022. "R Programming Workshop for the BMBF Conference on Research on Digitalisation for Cultural Education. "Analysing Social Media and Text Mining in R"." Friedrich-Alexander Universität Erlangen-Nürnberg, Nürnberg.
  • Breuer, Johannes, Stefan Jünger, and Veronika Batzdorfer. 2022. "GESIS Summer School in Survey Methodology. Introduction to R for Data Analysis." GESIS - Leibniz-Institut für Sozialwissenschaften, Köln.
  • Striewski, Sören, Olga Zagovora, and Isabella Peters. 2022. "Scientific Discourse on YouTube: Motivations for Citing Research in Comments." In Proceedings of the Association for Information Science and Technology, edited by Dirk Lewandowski, and Garrett Doherty, 59 1, 299-309. Hoboken, NJ: Wiley. doi: https://doi.org/10.1002/pra2.754.
  • Batzdorfer, Veronika, Holger Steinmetz, Marco Biella, and Meysam Alizadeh. 2022. "Conspiracy theories on Twitter: Emerging motifs and temporal dynamics during the COVID-19 pandemic." International Journal of Data Science and Analytics 13 315–333. doi: https://doi.org/10.1007/s41060-021-00298-6.