Dr.Larson presented the paper with NC State's Douglas Walls at the SIGDOC conferences in Silver Springs, MD on September 23.

This paper reports on the results of an intensive application development workshop held in the summer of 2015 during which a group of thirteen researchers came together to explore the use of machine-learning algorithms in technical communication. To do this they analyzed Amazon.com consumer electronic product customer reviews to reevaluate a central concept in North American Genre Theory: stable genre structures arise from recurring social actions. 

They discovered evidence of genre hybridity in the signals of instructional genres embedded into customer reviews. The paper discusses the creation of a prototype web application, “Use What You Choose” (UWYC), which sorts the natural language text of Amazon reviews into two categories: instructionally-weighed reviews (e.g., reviews that contain operational information about products) and non- instructionally-weighed reviews (those that evaluate the quality of the product). We developed the prototype application over approximately 48 hours using a modified version of the agile/scrum methodology. Our results contribute to rhetorical genre theory and offer ideas on applying genre theory to inform application design for users of information services.