Computational Social Science:
Advances in Data Collection and Access for Computational Social Science

Conference

2027 | Cologne

Advances in Data Collection and Access for Computational Social Science

Access to data for computational social science (CSS) research has undergone significant changes. Platform APIs and the scraping of digital behavioral data are becoming increasingly constrained, as online platforms have strong incentives to restrict public access to their data. At the same time, established modes of social science data collection face their own challenges, including declining survey participation rates, concerns about selective participation, non-response, hard-to-reach populations, the popularity of non-probability sampling approaches, costs, and declining trust in academic research by parts of the population.

In response to these transformations, computational social scientists have developed new approaches to collecting and accessing data. Advances in large language models create new opportunities for synthetic data generation and augmentation. Likewise, agentic approaches are facilitating web scraping and data collection from publicly accessible websites. The CSS community is developing new user-centered approaches to collect digital behavioral data such as data donations or dedicated smartphone apps. These methodological innovations unfold alongside evolving regulatory frameworks, especially the Digital Services Act (DSA), which creates new opportunities for researchers to access platform data and may help counteract the concentration of data in the hands of a few powerful actors. Together, these developments open up new pathways for researchers to access and collect diverse kinds of data, including but not limited to, digital behavioral data, social interactions, or mobility data.

The conference will address both methodological and practical questions related to data collection and access in CSS, including (but not limited to) the following topics:

  1. Innovative Tools and Infrastructures. This includes new approaches for collecting and accessing digital behavioral data, mobility data and other emerging data types through data donation, research apps, tracking tools, agentic or synthetic data collections, LLM-based simulations, web scraping, and infrastructures that reduce researchers’ dependence on platform providers.
  2. Data Governance, Regulatory and Institutionalized Models for Data Access. This includes researcher access under the DSA, platform-researcher institution partnerships, Trusted Research Environments, governance models, and sustainable arrangements for sharing and accessing sensitive or proprietary data.
  3. Quality, Validity, and Representativeness of Emerging Data Sources. This includes the validity and quality of synthetic, donated, or DSA-facilitated data, sample biases and representativeness, as well as open science and data sharing practices.
  4. Integration and Applications of Emerging Data Sources in CSS. This includes linking surveys with user-centered data collection, integrating and aggregating heterogeneous data sources, combining human-generated and synthetic data, and computational approaches for studying societal phenomena.

Call for Papers

We invite early career researchers to submit and present their CSS-related work to the DACH-CSS conference. Please submit an abstract of up to 300 words to workshop@computational-social-science.org by December 4th, 2026. Accepted contributors will give a short lightning talk (2 minutes) and present their work in greater detail during the poster session. We offer a limited amount of presentation slots, and submissions will be selected based on fitness with the field of CSS as well as the originality and quality of the work. Acceptance notifications will be sent out in January 2027.

As part of the event, we will confer a CSS Early Career Researcher Award (worth 500 EUR). All accepted submissions are automatically eligible. In addition to the conference’s core Advances in Data Collection and Access for Computational Social Science, we welcome submissions on topics including, but not limited to:

  • Best-practice applications of computational methods/big data to (complex) social science research questions
  • Work that combines established social science theories with digital data or/and computational methods
  • Empirical results and research designs from ongoing or completed CSS projects
  • Theoretical and conceptual challenges in CSS
  • CSS methodological innovations; including mixed-methods approaches
  • Data linkage applications and methods for enriching large-scale or online data sources