“Digital Humanities: the necessity of collaboration” – Gido Broers

In Melvin Wevers’ lecture Using Neural Networks to Study Conceptual Shifts in Text and Image, he explained why humanities researchers should collaborate with computer scientists. He did this by showing a neural network that can analyze and categorize images in advertisements. This network could then recognize certain patterns, which eventually could lead to new insights into, for instance, modes of representation throughout history.

Wevers’ research is an example of the digital turn in the humanities. Nowadays, it feels – at least for me – as if you cannot be a researcher in the field humanities without any digital knowledge. With digital knowledge, I refer to the knowing of how certain digital tools work and how they can be useful in searching through data or in generating relevant data for your research. If you know how to use these tools, you can search larger databases easier and faster and generate more data. Because more data becomes available digitally, our way of doing research changes, as José van Dijck rightfully points out in her article Big Data, Grand Challenges: On digitization and humanities research:

More data does not by itself mean more knowledge or better insights. In fact, it mainly means: more interpretation and the possibility to combine different methods. We want to be able to ask new meaningful questions and to substantiate possible answers with a range of sources (2016, 10).

An important aspect of this excerpt of Van Dijck’s article is the notion of ‘interpretation’, which relates to the reason why it is important that for instance, computer scientists collaborate with humanities scholars. Computer scientists can generate a large amount of data through complex digital tools, but those data are useless if you do not know how to interpret them. If we, humanities scholars, collaborate with computer scientists they can develop those digital tools in a way that the data comes out of those tools are relevant to our research.

The technical aspect of the neural network as explained by Wevers is still very vague for me. But it was interesting to see how a neural network is being trained and what the difficulties are that a computer scientist encounters while developing such a tool in order to make it useful for humanities researchers. This lecture emphasized for me that in interdisciplinary research it is not necessary to understand the other field completely, as long as you know a little bit of the struggles and pitfalls of the other discipline.

Sources

• Van Dijck, José. “Big Data, Grand Challenges: On digitization and humanities research.” KWALON 21.1 (2016): 8-18.