It's about methodology more than research questions, although they are of course linked. Incorporating digital methods in your humanities project, like GIS, 3D modeling or ABM, will quickly land you in digital humanities. Remember though, humanities have a lot of theory and methodology you might be unfamiliar with as a CS student, so teaming up with someone who has those skills but lack in programming etc. will synergize in this field.
this post was submitted on 02 Jun 2023
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You can spend the entire first year of a Dh degree trying to come up with a defition ;) for some it is humanities with digital methods, for me Dh is the study of the intersections between digital technology and society. So I'd work on decentralised networks, plus the ideology behind it. Also I do some web archiving, and try to argue for public access to web archives .
Have a look at https://journalofdigitalhistory.org/en , I think it's a good example of a Dh journal
I could be wrong, but there's the possibility that this refers to an education that is well rounded. Specifically, an education that teaches both the humanities and digital literacy. Someone graduating from this would be both a good reader, writer, citizen, and computer user.
I would assume it involves computer-related writings, history, etc. How people communicate online, hypertext fiction, wikis, the differences in how people write and present media online compared to on paper. How people have used memes, emoji, etc. Hacker lore. Some overlap with digital arts and social sciences - ethics, media creation
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According to the Wiki entry, beyond what KelsonV said, it also includes using digital techniques in the scholarship or analysis of humanities subjects. I imagine using generative models to explore how language develops in early societies or use audio analysis tools to study folk music.
Makes sense. But isn't something that a computer scientist can do anyway?
I work in the Digital Humanities and my experience is that typically Computer Science, Information Science and Data Science are not well prepared to work with Humanities data. Some commonplace challenges:
- the methodologies used in the humanities like semiotics, phenomenology, etc. often do not allow for the level of formalisation that a computer science model would require
- (probably a consequence of the above) data in the humanities is rarely quantitative and much more often qualitative, i.e. nominal and categorical if structured at all. That's why for example a lot of attention is paid recently to language models, but repeatedly we find out that these have undesirable (inadequate) biases
- a particularly big issue is that historical data is much more scarce than data scientists would like, and often it is not digitised or digitised with poor quality. As a consequence established machine learning approaches cannot be trained
There's much more to it, but these are the most immediate challenges that come to my mind.
There's been a lot of effort in creating intersectional degrees between CompSci and other fields. Yes a CS could do the analysis work, but they likely do not have the humanities driven education to construct the requirements for the analysis. Developing intersectional training can help develop a better bridge of understanding between the research design (i.e. the requirements) and the analysis or experiment design (i.e. the implementation). It's been a while since I was in school, but while I was leaving, this intersectional/interdisciplinary approach was growing in popularity, which led to the development of these sort of joint or dual degrees such as CS & Astronomy or Biology or Journalism.
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