Automated Content Analysis
The velocity with which scientific publications are being produced and made readily available has grown exponentially in the past decades. Traditional methods of literature synthesis depend on the manual handling of publications, which are inherently constrained by time and human effort, as well as human subjectivity. To address these limitations, researchers in social science fields and medical research have taken advantage of automated content analysis techniques to perform comprehensive literature reviews. Automated content analysis (ACA) refers to a suite of text-mining, machine-learning algorithms that use probabilistic models to identify and quantify the concepts and themes discussed in a body of literature. I introduced these tools to the broader ecological and evolutionary biology community in my article published in Methods in Ecology and Evolution. The publication of this article was followed by a blogpost in the journal’s Methods.blog. In my four publications featuring ACA, I have explored a variety of questions, such as “are sociecological challenges being addressed in forestry research?” and “how have research themes shifted in the past four decades of ecological research?” I plan to continue utilizing ACA to uncover exciting knowledge gaps in the literature that will guide future research directions.