Tracing the Human in Amazon Mechanical Turk through Rhetorical Text Mining

This study, written with Nathaniel Rivers, uses text mining to examine how the language of posts on Amazon’s Mechanical Turk (MTurk), an online global crowdsource marketplace, rhetorically articulates a model of the human that emerges from concrete labor acts rather than abstract moral or ethical capacities. The study analyzes 23,105 documents using multiple operations including weighted frequency counts, clusters and topic models, and frequency co-occurrence and n-gram diagrams at three grain sizes: the whole corpus; posts organized into five tiers based on compensation level; specific term associations. Findings identify coherent human aspects that collapse distinctions between bodily and intellectual capabilities, yet a separation between humans and non-humans is indistinct, because MTurk posts repeatedly redraw such boundaries, causing characteristic traits to be variably shared.