How does a semi-nomadic shepherd people on the border of Russia and Azerbaijan place themselves? How do twitter users challenge and transform institutional versions of events? How can citizens resist then narrow confines of reductive algorithmic assumptions on the internet? Questions such as these are not readily answered with traditional rhetorical methods, yet they recommend a rhetorical approach to their focus on meaning-making, constitutive community, and identity creation.

This project argues that Kenneth Burke’s method of cluster analysis can be profitably revived to meet rhetoric’s growing need for an approach that focuses on relationships, listens to vernacular voices, engages multiple texts, and considers the world from other viewpoints. The most commonly used approach to cluster analysis is a reductive equational form that is primarily concerned with the dissective, analytic half of cluster analysis. Reconstituting Burke’s more constructive, drawing-together form, this project develops a relational cluster analysis that centers in connections, community, and the relationships between words, people, and ideologies.

Relational cluster analysis’ effectiveness is demonstrated by use with ethnographic fieldnotes, a database of 5 million tweets, and the algorithmic infrastructure of Web 2.0. These exemplars demonstrate that, when applied at several layers of meaning, such as individual, community, dominant culture, and cross-cultural, this relational method is particularly generative in working with vernacular voices, community meanings, networked arguments, and digital cultures. Inductively listening to meaning-making foregrounds the subject, leading to substantial insight into not just individual but also community and cultural values and orientations. The elastic nature of a relationally-focused, multilevel cluster analysis affords the opportunity to gaze, engrossed, from others’ points of view.