Digital Humanities: What’s in it for us (philosophers)?

With the raise of Digital Humanities (DH), computational tools and techniques became available to support researchers in many areas of the Humanities. Philosophers, however, don’t seem too keen to embrace this development. Luckily, there are exceptions, and digital philosophy is getting off the ground. This is a very exciting development, because the first projects aimed at developing DH tools for philosophy showed that already with very basic computational methods, philosophers can gain a lot. In this post I will tell a bit about such a project in which I worked during my Master’s and after, which clearly showed that there is a big potential for digital methods in philosophy.

The project was called Phil@Scale, and in it philosophers teamed-up with computer scientists to develop computational tools for philosophers. Accordingly – and this was for me a very interesting aspect -, my primal task in this project was to reflect on my job as a philosopher. For in order to be able to develop computational tools which are to aid philosophers in their research, we have to know how can computers aid us. Thus, to begin with, I had to answer the question: what do I do, when I do philosophical research?

An important part of philosophical research, of course, is text-based. Philosophical research consists for a large part in close-reading of texts, which then serves as a basis for analyses and hypotheses on a conceptual level. This dependence on close-reading makes, first, that philosophical research is very slow and thus can be done only on a small scale, and second, that a lot depends on the interpretation of the text by the individual philosopher, which makes it a very qualitative and subjective area of research. It were those two aspects which we aimed to improve by means of computational tools in this project.

To aid philosophers in text-based research we developed a text-mining tool called SalVe. Text-mining is – roughly – a technique in which a text (the `corpus’; this can also be a collection of texts) is regarded as a data set, and by means of statistical methods certain kinds of information are extracted from it. SalVe has several functionalities and allows researchers to determine faster (compared to traditional methods) which parts of the textual corpus are relevant for their research, and moreover offers statistical (and thus in some important sense quantitative and objective) evidence for or against hypotheses.

Compared to other DH tools, SalVe is very basic, in the sense that it uses relatively simple techniques. This was a deliberate choice, since for philosophers it is very important to keep track of what the tool is doing exactly, and it is important that the tool does not do any interpretation of the text that philosophers want to do or should do themselves. In other words, we wanted SalVe to facilitate, rather than to replace, the work of a philosopher.

The first test-case for SalVe (which was part of my Master’s thesis) was Bernard Bolzano’s Wissenschaftslehre (Theory of Science). This is a work of about 2500 pages, so any help to master those is more than welcome. In fact, SalVe proved itself more than useful in answering a pretty technical interpretative question, namely the question whether according to Bolzano all analytic truths are grounded in a corresponding synthetic one (an hypothesis of De Jong (2001)). Not only did SalVe enable me to decide quicker which parts of the Wissenschaftslehre were relevant for my research question, and did it point me towards parts of the Wissenschaftslehre which I had not found using traditional methods, it also gave statistical evidence in favor of De Jong’s hypothesis.

A technical description of SalVe and a report of the results of the project Phil@Scale can be found in this article. The project showed clearly, in my opinion, that digital tools such as SalVe offer a welcome complementary improvement to the traditional way in which philosophical research is done. SalVe makes it possible to do philosophical research faster and makes this research more quantitative and objective, at least for corpora of a certain size. For sure within the history of philosophy, such tools can be highly useful, but I think other branches of philosophy can profit as well. What is needed in order to develop more, and more fancy computational tools for philosophy is that we, philosophers, think about the following: what we do, when we do philosophical research, and how could digital tools be of help? Any thoughts on this are warmly invited in the comments!