The rise of algorithmic analysis has been met by a rise in the interest in storytelling, suggesting that we are most human in the stories we tell, and that the stories we tell cannot be readily rendered into numbers. And so data scientists and digital humanities scholars have turned their attention to narrative forms in hopes of at least sketching out a computational model of narrative which might reveal how narratives work, at least as texts, if not also as vehicles for the delivery of meaning. Much of this work has, however, focused on texts like novels, skipping over the kinds of texts that most of us produce each and every day, both online and off.
This presentation surveys recent work in corpus stylistics, digital humanities, and information and data sciences, and then sketches out what might be a way to discern the shape of small stories. Examples are drawn from local legends about treasure, the clown legend cascade of 2016 and select literary works, among other things.
Dr. John Laudun, professor of English at the University of Louisiana at Lafayette, is “fascinated by how humans create their world with relatively simple resources.” His current work in culture analytics has brought collaborations with physicists and other scientists seeking to understand how texts can be modeled computationally in order to better describe their functions and features.