In the last few years, argument mining has emerged as a new eld that aims to identify argumentative portions in natural language text, and to uncover the structure of the underlying arguments. Domains that have been addressed include legal text, student essays, and customer reviews (as a follow-up step to sentiment analysis). In this talk, I suggest an annotation scheme for argumentation, and present results on automatic analysis of our argumentative microtext corpus - a collection of 115 short texts that have been produced by students in response to a trigger question, which usually bears the form \Should one (not) do X ?" I give results from a joint-inference approach to this task, present various extensions, and then discuss how the approach scales up to longer text.