The special issue collects four articles that aim to understand and address biases in computational social science. It was edited by Valerie Hase (University of Klagenfurt), Marko Bachl (FU Berlin) & Nathan TeBlunthuis (University of Texas at Austin).

The abstract to our editorial, titled Critical, but constructive: defining, detecting, and addressing bias in Computational Social Science explains what we tried to achieve with the special issue:

Computational Social Science (CSS) increasingly engages in critical discussions about bias in and through computational methods. Two developments drive this shift: first, the recognition of bias as a societal problem, as flawed CSS methods in socio-technical systems can perpetuate structural inequalities; and second, the field’s growing methodological resources, which create not only the opportunity but also the responsibility to confront bias. In this editorial to our Special Issue on CSS and bias, we introduce the contributions and outline a research agenda. In defining bias, we emphasize the importance of embracing epistemological pluralism while balancing the need for standardization with methodological diversity. Detecting bias requires stronger integration of bias detection into validation procedures and the establishment of shared metrics and thresholds across studies. Finally, addressing bias involves adapting established and emerging error-correction strategies from social science traditions to CSS, as well as leveraging bias as an analytical resource for revealing structural inequalities in society. Moving forward, progress in defining, detecting, and addressing bias will require both bottom-up engagement by researchers and top-down institutional support. This Special Issue positions bias as a central theme in CSS – one that the field now has both the tools and the obligation to address.

Hase, V., Bachl, M., & TeBlunthuis, N. (2025). Critical, but constructive: Defining, detecting, and addressing bias in Computational Social Science. Communication Methods and Measures, 19(4), 281–293. https://doi.org/10/g99vrm

The special issue includes an article by our colleague Miriam Siemon (FU Berlin) titled Beyond the binary? Automated gender classification of social media profiles.

All articles are freely available as open access publications.

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