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The recent turn towards quantitative text-as-data approaches in IR broughtnew ways to study the discursive landscape of world politics. Here seen ascomplementary to qualitative approaches, quantitative assessments have theadvantage of being able to order and make comprehensible vast amounts of text.However, the validity of unsupervised methods applied to the types of textavailable in large quantities needs to be established before they can speak toother studies relying on text and discourse as data. In this paper, weintroduce a new text corpus of United Nations Security Council (UNSC) speecheson Afghanistan between 2001 and 2017; we study this corpus through unsupervisedtopic modeling (LDA) with the central aim to validate the topic categories thatthe LDA identifies; and we discuss the added value, and complementarity, ofquantitative text-as-data approaches. We set-up two tests using mixed- methodapproaches. Firstly, we evaluate the identified topics by assessing whetherthey conform with previous qualitative work on the development of the situationin Afghanistan. Secondly, we use network analysis to study the underlyingsocial structures of what we will call 'speaker-topic relations' to see whetherthey correspondent to know divisions and coalitions in the UNSC. In both caseswe find that the unsupervised LDA indeed provides valid and valuable outputs.In addition, the mixed-method approaches themselves reveal interesting patternsdeserving future qualitative research. Amongst these are the coalition anddynamics around the 'women and human rights' topic as part of the UNSC debateson Afghanistan.

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