***Apologies for cross-posting

Dear colleagues,

we are excited to invite submissions for our upcoming session, "Bridging Methodology and Computational Social Sciencehttps://www.europeansurveyresearch.org/conf2025/sessions.php?sess=112", at the 11th conference of the European Survey Research Association (ESRA)https://www.europeansurveyresearch.org/conference/utrecht-2025/call-for-abstracts/, which takes place at Utrecht University, The Netherlands, from July 14 to July 18, 2025.

The session is organized by Joshua Claassen (DZHW, Leibniz University Hannover), Oriol J. Bosch (Oxford University), and Jan Karem Höhne (DZHW, Leibniz University Hannover). Our aim is to bring together innovative research at the intersection of methodology and Computational Social Science (CSS).

Please submit your abstract (max. 300 words) via the ESRA conference management systemhttps://www.europeansurveyresearch.org/conf2025/ by December 20, 2024.

Session Details In today's world, daily activities, work, and communication are continuously tracked via digital devices, generating highly granular data, including digital traces (e.g., app usage and browsing) and sensor data (e.g., geolocation). Researchers from various disciplines are increasingly utilizing these data sources, though often with different research objectives. Methodologists tend to focus on evaluating the quality and errors of digital data, while Computational Social Scientists (CSS) often leverage these data to answer more substantive research questions. However, there is a lack of collaboration between both worlds, resulting in a discipline divide.

For example, CSS researchers have embraced data donations, yet methodologists have not provided sufficient empirical evidence on the quality of such data. Moreover, web tracking data is rapidly being adopted in CSS, but methodological guidelines on how to gather the substantive content of website visits and apps (e.g., through HTML scraping) is lacking. However, there are methodological error frameworks covering both measurement and representation. These frameworks are yet to be (fully) leveraged.

This session invites contributions that bridge the gap between methodology and CSS, fostering collaboration across disciplines. We particularly welcome CSS work that incorporates a strong methodological foundation, as well as methodological research with clear relevance to substantive CSS inquiries. Topics may include, but are not limited to:

* Substantive research showcasing best practices when using digital data * Assessments of digital data in terms of quality and errors * Approaches reducing representation, sampling, and measurement errors of digital data * Studies substituting more traditional data collections (e.g., web surveys) with digital data (e.g., measuring opinions with digital traces) * Studies that go beyond the pure tracking (or donating) of app, search term, and URL data, including data integration and enrichment strategies

If you have any questions about the session, please do not hesitate to reach out.

We are looking forward to your submissions.

Best regards Joshua Claassen

German Centre for Higher Education Research and Science Studies (DZHW) Lange Laube 12 | 30159 Hannover | www.dzhw.euhttps://www.dzhw.eu/ | Germany

Joshua Claaßen (M.A.) Research Associate and PhD Candidate Member of CS3 Lab for Computational Survey and Social Science Research Area Research Infrastructure and Methods

Tel. +49 511 450670-407 | Fax +49 511 450670-960 E-Mail: claassen@dzhw.eumailto:claassen@dzhw.eu Twitter: @JClaass

Most recent publications: - Höhne, J.K., Claassen, J., Shahania, S., & Broneske, D. (in press). Bots in web survey interviews: A showcase. International Journal of Market Research. DOI: 10.1177/14707853241297009 - Höhne, J.K. & Claassen, J. (2024). Examining final comment questions with requests for written and oral answers. International Journal of Market Research. DOI: 10.1177/14707853241229329 - Höhne, J.K., Gavras, K., & Claassen, J. (2024). Typing or speaking? Comparing text and voice answers to open questions on sensitive topics in smartphone surveys. Social Science Computer Review. DOI: 10.1177/08944393231160961

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