A key strategy for scholars in the humanities is the close reading of cultural objects: reading to uncover layers of meaning that lead to deep comprehension. Close reading involves carefully reading and reflecting on a cultural object, in which we pay special attention to things like characterization, pacing, symbolism and imagery. It can also be put to use to uncover and engage critically with power relations that are inherent to each cultural object. The practice of close reading is embedded in a long scholarly tradition of hermeneutics: the theory and method of interpretation, involving a strong sense of reflexivity.
Close reading and related practices have recently been displaced by distant reading or machine reading, referring to reading methods that rely on programmatic modeling. This allows one to focus on units that are much smaller or much larger than the singular text itself – genres, devices, themes, tropes, and so on. Big data analytics, whose authority stems from its assumed access to the totality of a dataset (‘N=all’), are increasingly leveraged for the purposes of humanistic scholarship. This has led to questions about the value of close reading and its necessarily limited scope.
These questions about interpretation are characteristic of contemporary, data-driven society. There seems hardly any time to reflect on the many posts, articles or videos that are spread and seen at a breakneck speed. Virality and controversiality often seem more important than their truth value. Internet users, meanwhile, are thought to be trapped in ‘filter bubbles’ of their own algorithmically determined preferences, eradicating the possibility for critical attention and dialogue.
This course investigates the possibilities and pitfalls of hermeneutics in the context of datafied society. Based on a historical overview of hermeneutics, it asks: how can we critically assess the algorithmically driven and often controversial claims to knowledge to be found online? Using text analytics, we will analyze how knowledge and ideas are created and negotiated on social media platforms such as Reddit. In doing so, we will build skills in both natural language processing and close reading, in order to push back against problems of filter bubbles, post-truths, and alternative facts.
Please find the course book for 2019 below.