Analyses of seven writing studies journals, 2000–2019, Part II: Data-driven identification of keywords

Keywords are often used to shed light on shared words and their meanings, including their contestation. Often these keywords are determined using small samples or author inferences. However, identification of large sample, data-driven keywords is important for writing studies to avoid a range of biases including socioeconomic, confirmation, and sampling. We use the methodologies of “term frequency-inverse document frequency” (TF-IDF) and collocation on a corpus of journal articles from seven major writing studies journals: College Composition and Communication, College English, Computers and Composition, Research in the Teaching of English, Rhetoric Review, Rhetoric Society Quarterly, and Written Communication. By examining approximately 99% of the research articles published in these journals between 2000 and 2019 (N = 2738), we determine the evolution of keywords over time. Changes in keywords suggest attention to the impact of technology.