Contextualizing surveys: Incorporating digital trace and media data |
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| Coordinator 1 | Mr Manuel Holz (University of Technology Chemnitz) |
| Coordinator 2 | Miss Britta Maskow (University of Technology Chemnitz) |
| Coordinator 3 | Miss Arianna Fay Zehner (University of Technology Dresden) |
| Coordinator 4 | Professor Natalja Menold (University of Technology Dresden) |
| Coordinator 5 | Professor Jochen Mayerl (University of Technology Chemnitz) |
Contextualizing surveys: Incorporating digital trace and media data
Recent developments, methodological challenges and applications
The integration of survey and context data presents a significant opportunity to enhance analytical capabilities and provide more detailed insights into public opinion and social behavior. Traditionally, the contextual data used to complement survey data mostly consisted of time-invariant, country or geography-specific macro data. However, the advent of the digital age, characterized by new data forms like social media data and the digitalization of older formats (e.g., newspaper databases), offers innovative ways to contextualize probability sample surveys within their socio-political and historical contexts, thereby overcoming limitations associated e.g. with time-invariant contextual predictors.
By merging various data formats, researchers can achieve a more nuanced understanding of social phenomena. Nonetheless, this integration poses significant methodological and theoretical challenges. The goal of this session is to facilitate discussion on the practical implications of linking survey and contextual data formats within social science research.
We invite contributions that explore innovative methodological approaches to the collection and analysis of linked survey and contextual data. We particularly welcome submissions that utilize contextual data from traditional media sources (newspapers, television, etc.), digital media sources (social media platforms like Twitter, Instagram, Telegram, etc.), or digital trace data (web browsing data, mobile app usage, GPS location data, etc.). This list is not exhaus-tive, and we encourage contributions from other related fields as well.
Submissions should include studies that successfully combine both types of data to address important social science questions. Relevant topics include developing new tools, applying machine learning and computational methods for both data collection and data generation, integrating new data with traditional sources, harmonizing survey data and discussing statistical modelling techniques to accommodate the multitude of data sources.