Gender Gaps in Digital Spaces: The Computational Lens

Gender inequities exist across digital spaces (i.e., social media, online games, peer production) and manifest themselves in multiple ways (i.e., biased representation, unequal visibility, lack of engagement). Computational research is uniquely positioned to document these biases because it leverages large-scale data to identify patterns that would be difficult to detect and analyze otherwise. The analysis of these patterns can, in turn, inform interventions designed to alleviate the gaps where they are most salient.

This special issue seeks submissions carrying computational analyses of digital trace data to identify salient and emerging forms of gender divides. We are interested in studies that evaluate how technologies reinforce or create inequalities but also discuss how quantifying those patterns, and identifying their generative mechanisms, can help design better strategies to counter downstream effects on essential outcomes like opportunities, careers, or personal well-being.

We seek submissions that (1) present novel empirical evidence of how gender bias appears across digital contexts; (2) improve our theoretical understanding of the underlying mechanisms, social and technological; and (3) discuss how scholarship can move from documenting the bias to informing ways to correct it. Contributions will make use of computational tools and methodologies to uncover unique insights and assess the nature and prevalence of gender disparities, especially as they manifest in the form of large-scale behavioral patterns difficult to characterize using other analytical approaches. Successful submissions will also make a theoretical contribution in terms of how we think about underlying mechanisms driving the disparities and intervention strategies to reduce those gaps. Possible topics across empirical domains and digital contexts include but are not limited to:

• Representation gaps (i.e., bias in the coverage of knowledge databases);

• Inequalities in visibility and impact (i.e., centrality in collaboration networks);

• Algorithmic bias (i.e., using platform audits);

• Disparities in resources and opportunities (i.e., inequities in the allocation of funds and support);

• Bias in recognition and gaps in success (i.e., awards and promotions);

• Unequal effects of significant life events (i.e., impact of the pandemic).

The special issue will be published in the Journal of Computer-Mediated Communication (JCMC). JCMC is a fully open-access scholarly journal published by Oxford University Press on behalf of the International Communication Association. The journal publishes social science research on communicating using computer-based media technologies (see the current Editor’s Note and About the Journal for more insight into the journal’s mission and scope). JCMC is broadly interdisciplinary, publishing work by scholars in communication, business, education, political science, sociology, psychology, media studies, information science, and other disciplines.