Title: Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan,
Abstract: The recent turn towards quantitative text-as-data approaches in IR brought
new ways to study the discursive landscape of world politics. Here seen as
complementary to qualitative approaches, quantitative assessments have the
advantage of being able to order and make comprehensible vast amounts of text.
However, the validity of unsupervised methods applied to the types of text
available in large quantities needs to be established before they can speak to
other studies relying on text and discourse as data. In this paper, we
introduce a new text corpus of United Nations Security Council (UNSC) speeches
on Afghanistan between 2001 and 2017; we study this corpus through unsupervised
topic modeling (LDA) with the central aim to validate the topic categories that
the LDA identifies; and we discuss the added value, and complementarity, of
quantitative text-as-data approaches. We set-up two tests using mixed- method
approaches. Firstly, we evaluate the identified topics by assessing whether
they conform with previous qualitative work on the development of the situation
in Afghanistan. Secondly, we use network analysis to study the underlying
social structures of what we will call 'speaker-topic relations' to see whether
they correspondent to know divisions and coalitions in the UNSC. In both cases
we find that the unsupervised LDA indeed provides valid and valuable outputs.
In addition, the mixed-method approaches themselves reveal interesting patterns
deserving future qualitative research. Amongst these are the coalition and
dynamics around the 'women and human rights' topic as part of the UNSC debates
on Afghanistan.