References
- 1Abello, J., Broadwell, P., & Tangherlini, T. R. (2012). Computational folkloristics. Communications of the ACM, 55(7), 60–70. DOI: 10.1145/2209249.2209267
- 2Armaselu, F., Apostol, E.-S., Khan, F., Liebeskind, C., McGillivray, B., Truica, C.-O., Utka, A., Oleškevičienė, G. V., & van Erp, M. (2022). LL(O)D and NLP perspectives on semantic change for Humanities research. Semantic Web Journal, accepted for publication.
http://www.semantic-web-journal.net/system/files/swj2848.pdf . DOI: 10.3233/SW-222848 - 3Berezkin, Y. (2015). Spread of folklore motifs as a proxy for information exchange: Contact zones and borderlines in Eurasia. Trames, 19(1), 3–14. DOI: 10.3176/tr.2015.1.01
- 4Berezkin, Y. (2017).
Peopling of the New World from data on distributions of folklore motifs . In R. Kenna, M. MacCarron, & P. MacCarron (Eds.), Maths meets myths: Quantitative approaches to ancient narratives (pp. 71–89). Cham, Switzerland: Springer International Publishing. DOI: 10.1007/978-3-319-39445-9_5 - 5Bortolini, E., Pagani, L., Crema, E. R., Sarno, S., Barbieri, C., Boattini, A., Sazzini, M., da Silva, S. G., Martini, G., Metspalu, M., Pettener, D., Luiselli, D., & Tehrani, J. J. (2017). Inferring patterns of folktale diffusion using genomic data. Proceedings of the National Academy of Sciences, 114(34), 9140–9145. DOI: 10.1073/pnas.1614395114
- 6Burkert, W. (1982). Structure and history in Greek mythology and ritual. Berkeley, CA: University of California Press.
- 7Christiansen, R. (1992). The migratory legends: A proposed list of types with a systematic catalogue of the Norwegian variants. Helsinki: Suomalainen Tiedeakatemia, Academia Scientiarum Fennica.
- 8Darányi, S., & Lendvai, P. (Eds.). (2010). Proceedings of the first AMICUS workshop, October 21, 2010, Vienna, Austria. Szeged, Hungary: University of Szeged, Department of Library and Human Information Science.
- 9Darányi, S., Wittek, P., & Forró, L. (2012). Toward sequencing ‘Narrative DNA’: Tale types, motif strings and memetic pathways. Proceedings of CMN-12, 3rd workshop on Computational models of narrative in conjunction with the 8th Language resources and evaluation conference, 2–10.
http://narrative.csail.mit.edu/cmn12/proceedings.pdf - 10Darányi, S., Wittek, P., Konstantinidis, K., Papadopoulos, S., & Kontopoulos, E. (2016). A physical metaphor to study semantic drift. To appear in Proceedings of SuCCESS-16, 1st international workshop on Semantic change & evolving semantics. DOI: 10.48550/arXiv.1608.01298
- 11da Silva, S. G., & Tehrani, J. J. (2016). Comparative phylogenetic analyses uncover the ancient roots of Indo-European folktales. Royal Society Open Science, 3(1), 1–11. DOI: 10.1098/rsos.150645
- 12Dawkins, R. (1976). The selfish gene. Oxford, UK: Oxford University Press.
- 13Declerck, T., Aman, A., Banzer, M., Macháček, D., Schäfer, L., & Skachkova, N. (2017). Multilingual ontologies for the representation and processing of folktales. Proceedings of the First workshop on Language technology for Digital Humanities in Central and (South-)Eastern Europe, 20–23. DOI: 10.26615/978-954-452-046-5_003
- 14Declerck, T., Kostova, A., & Schäfer, L. (2017). Towards a linked data access to folktales classified by Thompson’s motifs and Aarne-Thompson-Uther’s types. Proceedings of Digital Humanities 2017.
https://www.dfki.de/fileadmin/user_upload/import/9028_Dh2017_LOD_TMI-ATU_final.pdf - 15Declerck, T., & Schäfer, L. (2017). Porting past classification schemes for narratives to a linked data framework. Proceedings of DATeCH2017. DOI: 10.1145/3078081.3078105
- 16Finlayson, A. M. (2016). Inferring Propp’s functions from semantically annotated text. Journal of American Folklore, 55–77. DOI: 10.5406/jamerfolk.129.511.0055
- 17Frenzel, E. (1992). Stoffe der Weltliteratur: Ein Lexikon dichtungsgeschichtlicher Längsschnitte (8. überarb. u. erweit. Aufl.). Stuttgart: Kröner.
- 18Gandrud, C. (2015). Reproducible research with R and RStudio (2nd ed.). Chapman and Hall/CRC. DOI: 10.1201/9781315382548
- 19Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2016). What does research reproducibility mean? Science Translational Medicine, 8(341), 341ps12. DOI: 10.1126/scitranslmed.aaf5027
- 20Harris, J. K., Johnson, K. J., Carothers, B. J., Combs, T. B., Luke, D. A., & Wang, X. (2018). Use of reproducible research practices in public health: A survey of public health analysts. PLoS ONE, 13(9). DOI: 10.1371/journal.pone.0202447
- 21Ilyefalvi, E. (2018). The theoretical, methodological and technical issues of digital folklore databases and computational folkloristics. Acta Ethnographica Hungarica, 63(1), 209–258. DOI: 10.1556/022.2018.63.1.11
- 22Karsdorp, F. (2016). Retelling stories: A computational-evolutionary perspective [PhD thesis]. Nijmegen: Radboud Universiteit.
- 23Karsdorp, F., & Fonteyn, L. (2019). Cultural entrenchment of folktales is encoded in language. Palgrave Communications, 5, 25. DOI: 10.1057/s41599-019-0234-9
- 24Karsdorp, F., & van den Bosch, A. (2013). Identifying motifs in folktales using topic models. In Proceedings of the 22 Annual Belgian-Dutch Conference on Machine Learning (pp. 41–49).
- 25Kestemont, M., Karsdorp, F., De Bruijn, E., Driscoll, M., Kapitan, K. A., Ó Macháin, P., Sawyer, D., Sleiderink, R., & Chao, A. (2022). Forgotten books: The application of unseen species models to the survival of culture. Science, 375(6582), 765–769. DOI: 10.1126/science.abl7655
- 26Kirk, G. S. (1970). Myth: Its meaning and functions in ancient and other cultures. Berkeley, CA: University of California Press. DOI: 10.1525/9780520342378
- 27Kontopoulos, E., Darányi, S., Wittek, P., Konstantinidis, K., Riga, M., Mitzias, P., Stavropoulos, T., Andreadis, S., Maronidis, A., Karakostas, A., & others. (2016a). Deliverable 4.5: Context-aware content interpretation. PERICLES project.
- 28Kontopoulos, E., Riga, M., Mitzias, P., Andreadis, S., Stavropoulos, T., Konstantinidis, K., Maronidis, A., Karakostas, A., Tachos, S., Kaltsa, V., & others. (2016b). Pericles deliverable 4.4: Modelling contextualised semantics. PERICLES project.
- 29Kühne, M., & Liehr, A. (2009). Improving the traditional information management in natural sciences. Data Science Journal, 8, 18–26. DOI: 10.2481/dsj.8.18
- 30Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of the 31st International conference on Machine Learning,
PMLR 32(2):1188–1196. DOI: 10.48550/arXiv.1405.4053 - 31Lô, G., de Boer, V., & van Aart, C. J. (2020). Exploring West African folk narrative texts using Machine Learning. Information, 11(5), 236. DOI: 10.3390/info11050236
- 32Marwick, B. (2017). Computational reproducibility in archaeological research: Basic principles and a case study of their implementation. Journal of Archaeological Method and Theory, 24, 424–450. DOI: 10.1007/s10816-015-9272-9
- 33McCullough, B. D. (2009). Open Access Economics journals and the market for reproducible economic research. Economic Analysis and Policy, 39(1), 117–126. DOI: 10.1016/S0313-5926(09)50047-1
- 34Meder, T. (2010). From a Dutch folktale database towards an international folktale database. Fabula, 51(1–2), 6–22.
- 35Meder, T., Karsdorp, F., Nguyen, D.-P., Theune, M., Trieschnigg, R. B., & Muiser, I. (2016). Automatic enrichment and classification of folktales in the Dutch Folktale Database. The Journal of American Folklore, 129(511), 78–96. DOI: 10.5406/jamerfolk.129.511.0078
- 36Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. DOI: 10.48550/arXiv.1301.3781
- 37Murphy, T. P. (2015). From fairy tale to film screenplay: Working with plot genotypes. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan UK. DOI: 10.1057/9781137552037
- 38Nguyen, D., Trieschnigg, D., Meder, T., & Theune, M. (2012). Automatic classification of folk narrative genres. In J. Jancsary (Ed.), Proceedings of KONVENS 2012 (pp. 378–382).
- 39Ofek, N., Darányi, S., & Rokach, L. (2013). Linking motif sequences with tale types by Machine Learning. In M. A. Finlayson, B. Fisseni, B. Löwe, & J. C. Meister (Eds.), 2013 Workshop on Computational Models of Narrative, 32, 166–182. DOI: 10.4230/OASIcs.CMN.2013.166
- 40Pasquier, T., Lau, M. K., Trisovic, A., Boose, E. R., Couturier, B., Crosas, M., Ellison, A. M., Gibson, V., Jones, C. R., & Seltzer, M. (2017). If these data could talk. Scientific Data, 4,
170114 . DOI: 10.1038/sdata.2017.114 - 41Pompeu, D. (2019). Interpretable Deep Learning methods for classifying folktales according to the Aarne-Thompson-Uther scheme. Master’s Thesis. Lisboa: Instituto Superior Técnico, Universidade de Lisboa.
- 42Qin, G., & Gao, L. (2012). An algorithm for network motif discovery in biological networks. IJDMB, 1–16. DOI: 10.1504/IJDMB.2012.045533
- 43Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using siamese BERT-networks. DOI: 10.18653/v1/D19-1410
- 44Reiter, N., Frank, A., & Hellwig, O. (2014). An NLP-based cross-document approach to narrative structure discovery. Literary and Linguistic Computing, 29(4), 583–605. DOI: 10.1093/llc/fqu055
- 45Ross, R. M., Greenhill, S. J., & Atkinson, Q. D. (2013). Population structure and cultural geography of a folktale in Europe. Proc R Soc B, 280:
20123065 . DOI: 10.1098/rspb.2012.3065 - 46Ross, R. M., & Atkinson, Q. D. (2015). Folktale transmission in the Arctic provides evidence for high bandwidth social learning among hunter-gatherer groups. Evolution and Human Behavior, 37(1), 47–53. DOI: 10.1016/j.evolhumbehav.2015.08.001
- 47Seigneuret, J. C. (1988). Dictionary of literary themes and motifs. New York, NY: Greenwood Press.
- 48Tangherlini, T. R. (2016). Big folklore: A special issue on computational folkloristics. The Journal of American Folklore, 129(511), 5–13. DOI: 10.5406/jamerfolk.129.511.0005
- 49Tangherlini, T. R., & Leonard, P. (2013). Trawling in the sea of the Great Unread: Sub-corpus topic modeling and Humanities research. Poetics, 41(6), 725–749. DOI: 10.1016/j.poetic.2013.08.002
- 50Tehrani, J. J. (2013). The phylogeny of Little Red Riding Hood. PLoS ONE, 8(11),
e78871 . DOI: 10.1371/journal.pone.0078871 - 51Tehrani, J. J., Nguyen, Q., & Roos, T. (2016). Oral fairy tale or literary fake? Investigating the origins of Little Red Riding Hood using phylogenetic network analysis. Digital Scholarship in the Humanities., 31(3), 611–636. DOI: 10.1093/llc/fqv016
- 52Thompson, S. (1951). Motif-index of folk-literature: A classification of narrative elements in folktales, ballads, myths, fables, mediaeval romances, example, fabliaux, jest-books and local legends (rev. [2nd] ed.). Copenhagen: Rosenkilde.
- 53Thompson, S. (1977). The Folktale. Berkeley, CA: University of California Press.
- 54Thuillard, M., d’Huy, J., Berezkin, Y., & Le Quellec, J.-L. (2018). A large-scale study of world myths. Trames, 22(4), 407–424. DOI: 10.3176/tr.2018.4.05
- 55Uther, H.-J. (2004). The types of international folktales: A classification and bibliography, based on the system of Antti Aarne and Stith Thompson. Helsinki: Suomalainen Tiedeakatemia, Academia Scientiarum Fennica.
- 56van der Loo, M. P. J. (2014). The stringdist package for approximate string matching. The R Journal, 6(1), 111–122. DOI: 10.32614/RJ-2014-011
- 57Vollan, B., & Ostrom, E. (2010). Cooperation and the Commons. Science, 330(6006), 923–924. DOI: 10.1126/science.1198349
- 58White, J. D. (1976). The analysis of music. New York, NY: Prentice-Hall.
- 59Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(1), 1–23. DOI: 10.18637/jss.v059.i10
- 60Winkler, W. E. (1990). String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey research methods, 354–359.
https://eric.ed.gov/?id=ED325505