Methods

Narrating history is perpetually contested, shaping and reshaping how nations and people understand both their pasts and the current moment. Measuring and evaluating the scope of histories is methodologically challenging. In this paper we provide a general approach and a specific method to measure historical recall.

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.

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.

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.

Introduction to Python for the social sciences and humanities

The City through Data, Texts, Maps, and Networks

Material for a 3 day workshop on computational text analysis for humanists and social scientists

Teaching material for a 2017 workshop in computational text analysis for humanists (and social scientists)