A Large-Scale Study of World Myths

Abstract

The study of the narrative elements in tales and myths (motifs) belongs to a long tradition, initially aimed at finding the area of origin of early narratives (Urtexts). This objective, which has been much criticized, is generally abandoned today, but is it possible to establish the basis for an objectively verifiable mythogeography? Computer technology enables sophisticated mathematical computations on databases of an unprecedented scale, which makes it possible to base the comparative mythology on replicable calculation processes. In order to check for several subsets of motifs that could be specific to particular zones or continents, we test here two new methods on a corpus of 2264 motifs from ca. 40.000 myths recorded among 934 peoples around the globe, and we show that these motifs are best classified into two main groups.

Extracting hierarchical features of cultural variation using network-based clustering

“…In the study of folklore and mythology, recurring plot patterns, or ‘motifs’, occur across cultures. Motif variation can provide insight into cross-cultural patterns, including migrations and cultural transmission in relation to ethnolinguistic barriers ( Berezkin, 2010 ; Bortolini et al, 2017 ; Korotayev et al, 2017 ; Thuillard et al, 2018 ). Here we used folklore motifs to analyse cultural variation, identifying motifs important in constructing the proposed hierarchical relationships.…”

“…In recent years, increasingly available large-scale datasets on aspects of variation across human cultures and within cultures over time have provided rich information about fine-scale details of human cultural variation and the factors that influence its dynamics ( Mesoudi, 2016 ; Kolodny et al, 2018 ). For example, investigations of variation in folktales among cultures have identified interactions of cultural diffusion and demic diffusion in the spread of folklore and mythology ( Bortolini et al, 2017 ; Thuillard et al, 2018 ). A study of design features of traditional canoes across Polynesian societies has suggested a faster rate of cultural change in canoe traits that were less significant to functional performance of the watercraft, in line with the faster evolution that occurs for non-functional rather than functional genetic variants ( Rogers & Ehrlich, 2008 ).…”

“…The analysis of complex data to reveal features of cultural variation makes use of a variety of statistical methods designed for high-dimensional data analysis more generally. Such methods include analyses of distance matrices based on cultural traits of interest ( Rogers & Ehrlich, 2008 ; Creanza et al, 2015 ; Bortolini et al, 2017 ; Thuillard et al, 2018 ), multivariate analysis techniques such as principal components analysis ( Creanza et al, 2015 ), correlations involving spatial statistics and geographic maps ( Atkinson, 2011 ; Creanza et al, 2015 ; Bortolini et al, 2017 ) and hierarchical tree-based clustering ( Creanza et al, 2015 ; Thuillard et al, 2018 ).…”

Bearing a Bag-of-Tales: An Open Corpus of Annotated Folktales for Reproducible Research

“…The convergence of two major trends in computational folkloristics (Abello, Broadwell, & Tangherlini, 2012) will likely shape the results of the next decade. The first is a focus on the evolutionary aspect of motif and/or tale type distributions, either with regard to certain tale types (Bortolini et al, 2017;Karsdorp, 2016;Karsdorp & van den Bosch, 2013;da Silva & Tehrani, 2016;Tehrani, 2013), or to the geographical distribution of globally occurring narrative motifs (Thuillard, d'Huy, Berezkin, & Le Quellec, 2018), even inferring the presence of lost narratives (Kestemont et al, 2022). A genetic metaphor seems to inform some approaches, perhaps inspired by the modelling capacities inherent in Dawkins' meme theory (Dawkins, 1976); these compare tale types as motif sequences to 'narrative DNA' (Darányi, Wittek, & Forró, 2012;Meder et al, 2016;Murphy, 2015;Ofek, Darányi, & Rokach, 2013), or look at the evolution of narrative/story networks as a quasi-biological process based on the mutation and recombination of narrative elements (Karsdorp, 2016;Karsdorp & Fonteyn, 2019), extended even to the framework of cultural evolution via population genetics (Ross, Greenhill & Atkinson, 2013;Ross & Atkinson, 2015).…”

“…The little one can say about the plethora of methods listed is that, regardless of the corpora, their regionality, and the analytical units whose distributions characterise the body of texts in question, they express similarity between items in terms of distance, with more similar items forming dense groups as the outcome of mass comparison. Cluster analysis (Thuillard et al, 2018), Principal Component Analysis (PCA) (Berezkin, 2015), Labelled Latent Dirichlet Allocation (L-LDA) (Karsdorp & van den Bosch, 2013), Support Vector Machines (SVM) (Nguyen et al, 2012;Meder et al, 2016), or deep learning by Recurrent Neural Networks (RNN) (Lô, de Boer, & van Aart, 2020), however, share the same nature of being static snapshots of collections. Hence there is a contradiction in principle in addressing text evolution, a dynamic phenomenon, through tools tailored to static measurements: the notion asks for vector fields instead of vector spaces (Darányi et al, 2016).…”