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Stories have the power to shape our identities and worldviews. They can be factual or fictional, text-based or visual and can take many forms—from novels and non-fiction to conspiracy theories, rumors and disinformation. We can characterize stories by their plot, their characters, their audience, their style, their themes or their purpose. Given the massive power of stories to alter the course of society, innovative methods to understand them empirically and quantitatively are necessary.
Today, we are pleased to introduce PLOS ONE’s Science of Stories Collection, which includes submissions invited through a Call for Papers last year. The Call for Papers welcomed primary research papers that propose solutions to real world, data-rich problems that use different empirical methods. The Guest Editors overseeing the scope and curating the Collection are Peter Dodds (University of Vermont), Mirta Galesic (Santa Fe Institute), Matthew Jockers (Washington State University), and Mohit Iyyer (University of Massachusetts Amherst).
At launch, the Collection includes over 15 papers illustrating data-driven approaches to understanding stories and their impact. Some articles explore the nature of narrative and narrative thinking in texts and other media, for instance, the role of similarity in narrative persuasion, the effects of choosing violence in narratives, the importance of characters in narratives communicating risk of natural disaster, the impact of storytelling in complex collaborative tasks such as food preparation, and the role of narrative in collaborative reasoning and intelligence analysis.
Other articles present new methods to extract stories from datasets and datasets from stories, including automated narrative analysis via machine learning, systematic modeling of narrative structure and dynamics, and large-scale analysis of gender stereotypes in movies and books.
A third group of papers analyze how narratives are transformed and how they can transform people, for example, looking at the co-evolution of contagion (e.g., disease, addiction, or rumor) and behavior, social media’s contribution to political misperceptions in US elections, how people’s intuitive theories of physics can partly account for how they think about imaginary worlds, how narrative can induce empathy for people engaging in negative health behaviors, and the impact of mental health recovery narratives on health outcomes.
A final group of papers explores the communication of data-rich narratives to the public, including the relative effectiveness of video abstracts and plain language summaries versus graphical abstracts and published abstracts, newly emerging platforms for writing and commenting on literary texts at unprecedented scale, and the role of narrative in perceived authenticity in science communication.
Papers will continue to be added to the Collection as they reach publication, so we invite you to revisit the Collection again for additional insights into the science of stories.
Peter Sheridan Dodds
Peter Dodds is Professor at the University of Vermont’s Department of Mathematics and Statistics. He is Director of the Vermont Complex Systems Center and co-runs the center’s Computational Story Lab. Having a general interest in stories and narratives, complexification, contagion, and robustness, Dodd’s research focuses on system-level, big data problems of all kinds, often networked, sociotechnical ones. His work has been supported by an NSF CAREER award to study sociotechnical phenomena, the McDonnell Foundation, the Office of Naval Research, NASA, the MITRE Corporation, Computer Associates, and Mass Mutual.
Mirta Galesic is Professor and Cowan Chair in Human Social Dynamics at the Santa Fe Institute, External Faculty at the Complexity Science Hub in Vienna, Austria, and Associate Researcher at the Harding Center for Risk Literacy at the Max Planck Institute for Human Development in Berlin, Germany. She studies how simple cognitive mechanisms interact with social and physical environments to produce seemingly complex social phenomena. She develops empirically grounded computational models of social judgments, social learning, collective problem solving, and opinion dynamics. She is also interested in how people understand and cope with uncertainty and complexity inherent in many everyday decisions.
Mohit Iyyer is an Assistant Professor in computer science at the University of Massachusetts, Amherst. Previously, he was a Young Investigator at the Allen Institute for Artificial Intelligence. Mohit obtained his PhD at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. His research interests lie in natural language processing and machine learning. Much of his work uses deep learning to model language at the discourse level, tackling problems like generating long coherent units of text, answering questions about documents and understanding narratives in fictional text.
Matthew L. Jockers
Matthew L. Jockers is Dean of the College of Arts & Sciences and Professor of English and Data Analytics at Washington State University in Pullman, WA. Jockers has been leveraging computation to understand narrative and style since the early 1990s. His books on the subject include Macroanalysis: Digital Methods and Literary History, Text Analysis with R for Students of Literature, and The Bestseller Code. In addition to his academic work, Jockers helped launch two text mining startups and worked as Principal Research Scientist and Software Development Engineer in iBooks at Apple.