Download fulltext HTML
Musgrave, S. (2019). Digital Humanities: New Tools and New Knowledge. KnE Social Sciences, 3(18), 808–818. https://doi.org/10.18502/kss.v3i18.4770

https://doi.org/10.18502/kss.v3i18.4770

statistics

  • 301 Abstract Views
  • 255 PDF Views
  • 0 HTML Views

authors

  • Simon Musgrave

    Simon Musgrave

    Monash University, Australia

Published Date

  • Jul 4, 2019

Digital Humanities: New Tools and New Knowledge

KnE Social Sciences / UNNES International Conference on Research Innovation and Commercialization 2018 / Pages 808–818

Abstract

A key aspect of the rapidly growing field of Digital Humanities is the application of computational tools to problems in humanistic research, a process which can lead to exciting new knowledge. I will illustrate this development with examples from my own research and from that of other scholars showing how the new tools are applicable across many areas of research in the humanities. In particular, I will discuss how the recent development of machine learning algorithms has made it possible to investigate more fully insights based on a theory of meaning (distributional semantics) which is over 60 years old. Although most of my discussion will focus on the application of new methods for research in the humanities, I will end by switching the perspective and considering how such approaches can enrich education in the humanities and produce graduates equipped with diverse skills which will serve them well in our digital world.

References
[1] Benkler, Yochai, Rob Faris & Hal Roberts. 2018. Network propaganda: manipulation, disinformation, and radicalization in American politics. New York, NY: Oxford University Press.


[2] Bowern, Claire. 2012. The riddle of Tasmanian languages. Proceedings of the Royal Society of London B: Biological Sciences 279(1747). 4590–4595. doi:10.1098/rspb.2012.1842.


[3] Busa, Roberto. 1980. The annals of humanities computing: The index thomisticus. Computers and the Humanities 14(2). 83–90.


[4] Dixon, Robert M. W. 1980. The languages of Australia (Cambridge Language Surveys). Cambridge, Eng; New York: Cambridge University Press.


[5] Dourish, Paul & Genevieve Bell. 2011. Divining a digital future: Mess and mythology in ubiquitous computing. Cambridge MA: MIT Press.


[6] Eshet-Alkalai, Yoram. 2004. Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia 13(1). 93.


[7] Firth, J.R. 1968. A synopsis of linguistic theory 1930-1955. In F. R. Palmer (ed.), Selected Papers of J.R. Firth 1952-1959, 168–205. London: Longman.


[8] Gold, Valentin, Mennatallah El-Assady, Tina Bögel, Christian Rohrdantz, Miriam Butt, Katharina Holzinger & Daniel Keim. 2015. Visual Linguistic Analysis of Political Discussions: Measuring Deliberative Quality. Digital Scholarship in the Humanities fqv033. doi:10.1093/llc/fqv033.


[9] Harris, Zellig S. 1954. Distributional Structure. WORD 10(2–3). 146–162. doi:10.1080/00437956.1954.11659520.


[10] Holland, Barbara R., Katharina T. Huber, Vincent Moulton & Peter J. Lockhart. 2004. Using Consensus Networks to Visualize Contradictory Evidence for Species Phylogeny. Molecular Biology and Evolution 21(7). 1459–1461. doi:10.1093/molbev/msh145.


[11] Kenderdine, Sarah. 2013. “Pure Land”: Inhabiting the Mogao Caves at Dunhuang. Curator: The Museum Journal 56(2). 199–218. doi:10.1111/cura.12020.


[12] Long, Hoyt & Richard Jean So. 2016. Literary pattern recognition: Modernism between close reading and machine learning. Critical Inquiry 42(2). 235–267.


[13] McCarty, Willard. 2004. The Analytical Onomasticon. An Analytical Onomasticon to the Metamorphoses of Ovid. http://www.mccarty.org.uk/analyticalonomasticon/ (2 November, 2018).


[14] McEnery, Tony & Helen Baker. 2017. Corpus linguistics and 17th-century prostitution: computational linguistics and history (Corpus and Discourse. Research in Corpus and Discourse). London; New York, NY: Bloomsbury Academic. http://ezproxy.lib.monash.edu.au/login?url=http://oapen.org/download?type= document&docid=625761 (9 July, 2018).


[15] Mikolov, Tomas, Kai Chen, Greg Corrado & Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.


[16] Pereira, Francisco, Bin Lou, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Matthew Botvinick & Evelina Fedorenko. 2018. Toward a universal decoder of linguistic meaning from brain activation. Nature Communications 9(1). doi:10.1038/s41467-018-03068-4. http://www.nature.com/articles/s41467-018- 03068-4 (7 March, 2018).


[17] Rajeg, Gede Primahadi Wijaya, Karlina Denistia & Simon Musgrave. 2018. Semantic Vector Space Model and the usage patterns of Indonesian denominal verbs. The Twenty-Second International Symposium On Malay/Indonesian Linguistics (ISMIL 22), UCLA (11-12 May 2018). doi:doi.org/10.4225/03/5acffc60eb649.


[18] Ryan, Lyndall. 2012. Tasmanian Aborigines: a history since 1803. Crows Nest, N.S.W: Allen & Unwin.


[19] Tredinnick, Luke. 2008. Digital information culture: the individual and society in the digital age. Elsevier.


[20] Van Dijk, Jan & Kenneth Hacker. 2003. The digital divide as a complex and dynamic phenomenon. The information society 19(4). 315–326.


[21] Whitehead, Alfred North. 1929. Process and Reality, an Essay in Cosmology, by Alfred North Whitehead,... The University Press.


[22] Xiao, Richard & Tony McEnery. 2006. Collocation, Semantic Prosody, and Near Synonymy: A Cross-Linguistic Perspective. Applied Linguistics 27(1). 103–129. doi:10.1093/applin/ami045.