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What the hell are Digital Humanities? (lemmy.ml)

submitted 1 year ago by DJDSXSHOWFX@lemmy.ml to c/technology@lemmy.ml

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Hello guys, I am a CS engineer and from time to time I see this term "Digital Humanities" thrown around. After a few internet search I still haven't understood.

Do you know what is it all about?

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[–] projectazar@lemmy.ml 4 points 1 year ago (3 children)

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.

[–] DJDSXSHOWFX@lemmy.ml 2 points 1 year ago (2 children)

Makes sense. But isn't something that a computer scientist can do anyway?

[–] projectazar@lemmy.ml 2 points 1 year ago* (last edited 1 year ago)

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.

[–] mapto@lemmy.world 2 points 9 months ago

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.