The study of space politics is presently inhibited by a paucity of available data. This paper introduces spaceTexts, a new dataset of speeches in the UN Committee on the Peaceful Uses of Outer Space. These yearly statements in the preeminent forum for space diplomacy shed new light on the positions and preferences of state and nonstate actors on a topic which has received relatively scant political science attention. The corpus is described and explored through unsupervised machine learning methods, namely structural topic models. The dataset is relevant to space policy and natural language processing research, as well as any scholar interested in the employment of textual data to better understand UN politics, the formation of international law and arms control regimes, and the evolution of dimensions of diplomatic conflict in international relations.