Data Visualization This visualization shows the evolution of the Little Red Riding Hood folk tale

Published on November 20th, 2013 | by Travis Korte


Modeling the Evolution of Little Red Riding Hood

Research published last week in the open access journal PLoS ONE from Durham University in England uses mathematical modeling and phylogenetic analysis to probe and visualize the evolutions of folk tales. The research, conducted by Durham anthropology Lecturer Jamie Tehrani, focused on Little Red Riding Hood, a story popularized in Western Europe by the Brothers Grimm in the 19th century. Dr. Tehrani found that the story shares a common root with another folk tale, The Wolf and the Kids, which dates back to the 1st century A.D.

Drawing from a dataset of 58 variants of the folk tales, the analysis used 72 plot variables that included characteristics of the protagonsts, villain and ultimate outcome of the story. To quantify the evolution of the story, Dr. Tehrani used phylogenetics, a set of mathematical techniques borrowed from evolutionary biology, to compare the similarities of plot variables and give probabilities that a given pair of stories has the same origin. The research helps settle a longstanding debate about whether Little Red Riding Hood had actually descended from a Chinese folk tale; in fact, the new analysis shows that the Chinese version likely descended from the European version, not vice versa.

Take a look.

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About the Author

Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.

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