Introducing PLOS ONE’s Science of Stories Collection

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.

Guest Editors

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

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

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.


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Introducing the Targeted Anticancer Therapies and Precision Medicine in Cancer Collection

  While the rate of death from cancer has been declining since the 1990s, an estimated 9.6 million people died from cancer in 2018, making it the second-leading cause of death worldwide [1]. According to

‘Wicked problems’ and how to solve them

In this Guest Blog, PLOS ONE Academic Editor, Sieglinde Snapp, discusses the challenges faced in sustainability research to solve complex, so-called “Wicked Problems”, and how conferences such as Tropentag are bringing together researchers from multiple

A Big Paper for a Tiny Dinosaur

In paleontology, the fossil is the basic data point for any research, regardless of the amount of technology used. Consequently, descriptions of a fossil’s anatomy are critical for scientists answering a variety of questions. What species is this animal? Look to the fossil. What did it eat? Look at the teeth. Where does the animal fit on the evolutionary tree? Compare its fossil with other fossils. Detailed documentation and description of a specimen isn’t particularly glamorous, but absolutely necessary.

The tiny plant-eating dinosaur Fruitadens scurried through the underbrush of Colorado around 150 million years ago, long before the rise of the Rocky Mountains. First named in a brief article in 2010, Fruitadens made a splash for its diminutive length of less than 1 meter and estimated body mass of under 1 kilogram. Unfortunately, the original publication did not have space for more than a general anatomical description as well as confirmation that Fruitadens’ small size wasn’t because it was “just” a baby of a larger species. Thus, a new paper in PLoS ONE by Richard Butler, Laura Porro, Peter Galton, and Luis Chiappe fills in many of the essential details.

Artist’s reconstruction of Fruitadens. By Smokeybjb, licensed under Creative Commons Attribution-Share Alike 3.0 Unported license.

Fruitadens belonged to an unusual, widespread, and rare group of dinosaurs called heterodontosaurids. They first appeared around 200 million years ago in South Africa, and persisted until around 140 million years ago in England. Heterodontosaurids were small (no more than 2 meters in maximum body length) and characterized by unusual fangs at the front of their jaws. Fruitadens was no exception—although its lower jaw is incomplete, the preserved portion of the teeth shows that it too probaby had fangs. The rest of the teeth are more conventional, similar to those seen in other small plant-eating dinosaurs.

So, how did Fruitadens and other heterodontosaurids use their tiny, fanged jaws? The researchers developed simple two-dimensional models of the jaws in heterodontosaurids, reconstructing the movements associated with the bones and muscles. A basic difference between early and late-surviving heterodontosaurids (including Fruitadens) was identified. Specifically, Fruitadens and its close relatives had simpler jaw anatomy than their ancestors, suggestive of a switch to simpler, weaker, and more rapid jaw movements. Although much more work remains, Butler and colleagues suggest that Fruitadens may have been an ecological generalist subsisting on a variety of plants, insects, and other small organisms. This contrasts with the diet of its ancestors, subsisting primarily on plants.

A reconstruction of the skull of Fruitadens, from Butler et al. 2012.

Because they are so small, heterodontosaurid fossil are pretty scarce, and details of their evolutionary relationships are sketchy. Butler and colleagues carefully documented all of the relevant anatomical details in Fruitadens through photographs, CT scans, and text. In the process, the researchers identify some previously unrecognized features that characterize heterodontosaurids as a whole, and other formerly recognized features that do not. Although much work remains—particularly through the collection and description of new fossils—this new paper is an important step towards better understanding Fruitadens and its enigmatic kin.


Butler RJ, Galton PM, Porro LB, Chiappe LM, Henderson DM, Erickson GM (2010) Lower limits of ornithischian dinosaur body size inferred from a diminutive new Upper Jurassic heterodontosaurid from North America. Proc Roy Soc B 277: 375–381.

Butler RJ, Porro LB, Galton PM, Chiappe LM (2012) Anatomy and cranial functional morphology of the small-bodied dinosaur Fruitadens haagarorum from the Upper Jurassic of the USA. PLoS ONE 7(4): e31556. doi:10.1371/journal.pone.0031556


Top image from, licensed under Creative Commons Attribution-Share Alike 3.0 Unported license.

Bottom image from Butler et al. 2012, Figure 1.

About the Author: Dr. Andrew Farke is a vertebrate paleontologist and an academic editor at PLoS ONE. He handled the manuscript described in this post. Andy also has a blog, The Open Source Paleontologist and can be followed via Twitter @andyfarke.

Multivariate Versus Univariate Conceptions of Sex Differences: Let the Contest Begin

Richard A. Lippa

The following guest post is written by Professor of Psychology, Richard A. Lippa. Dr. Lippa is a professor at California State University, Fullerton and is also a peer reviewer for PLoS ONE. In the following opinion piece, he comments on the paper, The Distance Between Mars and Venus: Measuring global sex differences in personality, which published in PLoS ONE today.

In their paper, “The Distance Between Mars and Venus: Measuring global sex differences in personality,” Del Giudice, Booth, and Irwing offer an interesting new perspective on sex differences and a useful critique of Hyde’s gender similarities hypothesis [1]. At core, Del Giudice and his colleagues ask: What is the proper metric to use when assessing sex differences in multivariate domains? They nominate the Mahalanobis D statistic—the multivariate generalization of the d statistic—as the best metric to assess sex differences in multi-trait individual differences domains such as personality, cognitive abilities, and interests, and they show empirically that, while on-average sex differences in traits from a given domain (e.g., personality) may be relatively small, the multivariate effect size (D) can simultaneously be quite large.

By way of analogy, consider sex differences in body shape. The Hyde “gender similarities” approach would assess specific traits—e.g., shoulder-waist ratios, waist-hip ratios, torso-to-leg-length ratios, etc.—and then average the d values across these traits, to arrive at the likely conclusion that men and women are more similar than different in body shape. In contrast, the Del Giudice, Booth, and Irwing multivariate approach would more likely generate the conclusion that sex differences in human body shape are quite large, with men and women having distinct multivariate distributions that overlap very little.

Which conclusion is correct? Although there are no God-given prescriptions for proper metrics of effect size, my guess is that lay people would agree more with the Mahalonobis D than with the “mean d” result—i.e., if asked to classify actual human body outlines as “male” or “female,” lay people would likely achieve extremely high levels of accuracy by intuitively aggregating across various body-shape dimensions and making “multivariate,” configural judgments, despite the fact that ds for some individual body traits might be low.

In advocating the use of the Mahalanobis D statistic, Del Giudice, Booth, and Irwing seem, to me, to be advocating the notion that sex differences in various domains are often multivariate and configural in nature. Such a multivariate approach is especially important in research that explores how well sex differences in personality, cognitive abilities, and interests predict sex differences in real-life criteria, such as participation in STEM (science, technology, engineering, and math) fields, susceptibility to mental and physical illnesses, and the tendency to engage in antisocial behaviors.

For example, to adequately explain men’s and women’s different participation in STEM fields, researchers need to consider sex differences in a variety of cognitive ability domains: various visuospatial skills, math abilities, mechanical aptitudes, and so on. A still more complete account would focus on sex differences in interests and personality as well. Men’s interests are, on average, considerably more thing-oriented and less people-oriented than women’s interest are, and women exceed men some on personality traits (e.g., agreeableness, warmth) that may not always find satisfying expression in STEM fields [2, 3].

This discussion of predicting real-life criteria leads to the two additional methodological recommendations made by Del Giudice, Booth, and Irwing: When assessing sex differences in psychological traits, researchers should ensure that (1) trait measures are reliable, and (2) traits are measured at the proper level of specificity. Regarding point (1): Although many gender researchers may not have the statistical expertise or inclination to compute latent factor measures, they nonetheless need to recognize that unreliable trait measures can attenuate sex differences and they must statistically correct for the unreliability of measures, when possible [4].

One nice feature of Del Guidice, Booth, and Irwing’s recommendations is that they can be put to an empirical test. This can be illustrated by research on how well sex differences in personality account for sex differences in antisocial behavior [5]. Del Giudice, Booth, and Irwing suggest that, because of their finer resolution, Big Five facet scores will predict sex differences in antisocial behavior better than Big Five factor scores. This is a testable proposition. They also suggest that when researchers predict sex differences in antisocial behavior from personality measures, they need to employ a multivariate approach to personality. Research shows that sex differences in a number of personality traits—e.g., components of agreeableness, conscientiousness, and neuroticism—contribute to sex differences in antisocial behavior [5]. Thus, the large sex differences in antisocial behavior that are apparent in everyday life probably reflect large multivariate sex differences in personality (in keeping with Del Guidice, Booth, and Irwing’s approach). Clearly, the power of the multivariate approach to predict sex differences in criteria such as antisocial behavior is open to empirical investigation.

It is ironic that while the “gender similarities hypothesis” has gained currency among some psychologists, many biological and medical researchers appear to be moving in the opposite direction, increasingly emphasizing the importance of sex differences in various physiological and disease processes [6]. Would biological and medical researchers entertain the Hydean proposition that “males and females are similar on most, but not all, biological variables”?  On some level, this assertion seems to be true but, as Del Giudice, Booth, and Irwing note, its truth value depends critically on the specific domain of sex differences under study and on the metric of similarity and difference that researchers use. In practical terms, Hyde’s vague “gender similarities hypothesis” will probably provide cold comfort to men and women seeking sound and specific medical advice concerning their heart disease, autoimmune disorders, or medication levels. In biology and medicine, as in psychology, I believe it will prove useful to take a multivariate approach to sex-linked traits in various domains, to acknowledge that some sex differences are small while others are large, and to keep one’s eye on the criteria that need to be predicted rather than on broad ideological statements.

Del Giudice, Booth, and Irwing’s title employs the much-used “Mars and Venus” metaphor, suggesting a seemingly astronomical separation between the sexes. This is undoubtedly an exaggeration, reflecting a kind of poetic license. Hyde prefers to speak of the distance between North Dakota and South Dakota. However, her metaphor may, inadvertently, reflect a truth she is unwilling to acknowledge: that if you travel from the multivariate “centroid” of one state to the other, you’ll still have a mighty long way to walk.


1. Hyde JS (2005). The gender similarities hypothesis. Amer Psychologist 60: 581-592.
2.Lippa RA (2005). Gender, nature, and nurture. Mahwah, NJ: Lawrence Erlbaum Associates.
3.Su R, Rounds J, Armstrong PI (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psych Bull, 135, 859-884.
4.Lippa RA (2006). The gender reality hypothesis. Amer. Psychologist 61: 639-640.
5.Moffit TE, Caspi A, Rutter M, Silva PA (2001). Sex differences in antisocial behavior. Cambridge, England: Cambridge University Press.
6.Blair ML (2007). Sex-based differences in physiology: What should we teach in the medical curriculum? Adv Physiol Educ, 31, 23-25.