Methods

In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements.

Differences between computationally generated and human-generated themes in unstructured text are important to understand yet difficult to assess formally. In this study, we bridge these approaches, comparing topic models to hand-generated categories and comparing two different topic modelling solutions.

In presenting the contributions to a special issue, we discuss several insights that emerge from this work, which hold relevance not only for current and aspiring practitioners of computational text analysis, but also for its skeptics.

Starting from the premise that objectivity in knowledge creation is a worthy—even utopian—pursuit, this essay argues that computational methods are aligned with embodied objectivity and the situated knowledges and partial perspectives framework proposed by Donna Haraway.

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.

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.

Introduction to Python for the social sciences and humanities