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I empirically demonstrate the alignment between machine learning and inductive research through a word embedding model of first-person narratives of the nineteenth-century U.S. South.

Using a combination of network analysis, computational text analysis, and qualitative interpretation, the author examines women’s movement discourses across the first and second waves of activism and between two sites, New York City and Chicago. Place, she finds, serves to capture differences in political logics that generate durable differences within movement discourse.

Using an inductive, theory-building approach and combination of computational and qualitative methods, we investigate how the concept of implicit bias was translated into practice through the ADVANCE program and identify five key features that made implicit bias useful as a change framework in the academic STEM setting.

This article compares three common computer-assisted approaches—dictionary, supervised machine learning, and unsupervised machine learning—to those produced through a rigorous hand-coding analysis of inequality in the news.

Using the U.S. environmental movement as a case study, and employing a data-driven and inductive strategy that combines both computational and qualitative methods, we find that strategy emerges as organizations link their actions to their goal orientation: what level of society the organization views as the locus of change.

This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis.

In an attempt to reorient the field toward a new standard for measuring meaning in big data, one based on transparency and replicability, I propose five guidelines to evaluate any text-analysis project.

In the lead up to International Women’s Day on Friday March 8th, SAGE Ocean posed a series of questions to leading academics. In this post I explores how we nurture an academic landscape that is more accessible to women.

In this blog post I explore a fundamental question in computational social science: does “big computation” necessarily comes at the expense of narrative richness, contextual detail, and theory? Or, conversely, narrative richness can only happen without computation?

Rather than simply calling for diversity within the field of data analytics on terms dictated by this “new ‘Old Boy Network’ of Ivy Leaguers,” we should instead make big data, and data analytics, work on our own terms. I believe we can develop a data analytics that helps us touch, smell, and feel the game (and the social world).