Computational Analysis of the Body in European Fairy Tales

This article explores how digital humanities research methods can be used to analyze the representations of gendered bodies in European fairy tales, a flexible and pervasive genre that has influenced Western children\u27s education and acquisition of gender identity for centuries. By blending the theoretical and methodological concerns of folkloristics, gender studies, and large-scale scientific research, this article demonstrates the utility of cross-disciplinary collaboration in asking traditional questions of traditional materials with new methods. To facilitate this research, a hand-coded database listing every reference to a body or body part in the 233 fairy tales was created. Analysis revealed strong indications that the gender and age of fairy-tale protagonists correlate in ways that indicate societal value being placed on certain perspectives, with youthful and masculine perspectives being validated as universal, whereas feminine and aged bodies are often marked as ‘other’. Feminist scholars have articulated some of these ideas in the past, and this approach allows for a more empirical exploration of such claims

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

UIDB/00657/2020 UIDP/00657/2020This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.publishersversionpublishe

On how religions could accidentally incite lies and violence: Folktales as a cultural transmitter

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character (βT_and_Lie_O= 2.23; βC_and_Lie_O= 1.47; βT_and_Lie_O= 2.23). A violent act committed to serving Buddhist missions results in a happy ending for the committer (βB_and_Viol_O= 2.55). What is highlighted here is a glaring double standard in the interpretation and practice of the three teachings: the very virtuous outcomes being preached, whether that be compassion and meditation in Buddhism, societal order in Confucianism, or natural harmony in Taoism, appear to accommodate two universal vices—violence in Buddhism and lying in the latter two. These findings contribute to a host of studies aimed at making sense of contradictory human behaviors, adding the role of religious teachings in addition to cognition in belief maintenance and motivated reasoning in discounting counterargument

On how religions could accidentally incite lies and violence: folktales as a cultural transmitter

Folklore has a critical role as a cultural transmitter, all the while being a socially accepted medium for the expressions of culturally contradicting wishes and conducts. In this study of Vietnamese folktales, through the use of Bayesian multilevel modeling and the Markov chain Monte Carlo technique, we offer empirical evidence for how the interplay between religious teachings (Confucianism, Buddhism, and Taoism) and deviant behaviors (lying and violence) could affect a folktale’s outcome. The findings indicate that characters who lie and/or commit violent acts tend to have bad endings, as intuition would dictate, but when they are associated with any of the above Three Teachings, the final endings may vary. Positive outcomes are seen in cases where characters associated with Confucianism lie and characters associated with Buddhism act violently. The results supplement the worldwide literature on discrepancies between folklore and real-life conduct, as well as on the contradictory human behaviors vis-à-vis religious teachings. Overall, the study highlights the complexity of human decision-making, especially beyond the folklore realm

The Road to Quantum Computational Supremacy

We present an idiosyncratic view of the race for quantum computational supremacy. Google's approach and IBM challenge are examined. An unexpected side-effect of the race is the significant progress in designing fast classical algorithms. Quantum supremacy, if achieved, won't make classical computing obsolete.Comment: 15 pages, 1 figur

ProppML: A Complete Annotation Scheme for Proppian Morphologies

We give a preliminary description of ProppML, an annotation scheme designed to capture all the components of a Proppian-style morphological analysis of narratives. This work represents the first fully complete annotation scheme for Proppian morphologies, going beyond previous annotation schemes such as PftML, ProppOnto, Bod et al., and our own prior work. Using ProppML we have annotated Propp\u27s morphology on fifteen tales (18,862 words) drawn from his original corpus of Russian folktales. This is a significantly larger set of data than annotated in previous studies. This pilot corpus was constructed via double annotation by two highly trained annotators, whose annotations were then combined after discussion with a third highly trained adjudicator, resulting in gold standard data which is appropriate for training machine learning algorithms. Agreement measures calculated between both annotators show very good agreement (F_1>0.75, kappa>0.9 for functions; F_1>0.6 for moves; and F_1>0.8, kappa>0.6 for dramatis personae). This is the first robust demonstration of reliable annotation of Propp\u27s system

Heroes, Villains, and the In-Between: A Natural Language Processing Approach to Fairy Tales

While great strides have been made with natural language processing (NLP) techniques in the last few decades, there has been a notable lack of research into utilizing NLP for the genre of fiction. This project seeks to address this gap by considering the use of NLP techniques for the summarization of European fairy tales. This subgenre of fiction is an appropriate starting point for investigation due to its archetypal characters and relatively simple story arcs. My approach is to extract the main characters of texts, along with key descriptors in the form of modifying adjectives and verbal actions the characters take part in. Through this method, I suggest how we may parse characters into Proppian archetypes by tracking their probabilistic association with certain linguistic occurrences. This classification schema in turn makes possible the broader classification of fairy tales into types. The model has an overall F1 score of 0.77, the individual parts having F1 scores of 0.89, 0.75, and 0.66 for character retrieval, adjective extraction, and verb extraction, respectively. This project may also be extended further, laying key groundwork for further automatization of categorization of characters and ultimately stories themselves