Perspectives on Science seminar 18.11.

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling – a popular unsupervised approach in the social sciences – it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora, and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling cannot provide interpretation-independent evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes towards topic modeling, called topic realism and topic instrumentalism. It argues that under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the objectivity of unsupervised text analysis cannot amount to a mechanical elimination of the researcher’s judgments from interpretive processes. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work.