Public discourse on highly charged, complex social and political issues is extensive, with millions of sentences available for analysis. It is also rife with metaphors that manifest vast numbers of novel metaphoric expressions. More and more, to understand such issues, to see who is saying what and why, we require big data and statistically-based analysis of such corpora. However, statistically-based data processing alone cannot do all the work. The MetaNet (MN) project has developed an analysis method that formalizes existing insights about the conceptual metaphors underlying linguistic expressions into a computationally tractable mechanism for automatically discovering new metaphoric expressions in texts. The ontology used for this computational method is organized in terms of metaphor cascades, i.e. pre-existing packages of hierarchically organized primary and general metaphors that occur together. The current paper describes the architecture of metaphor-to-metaphor relations built into this system. MN’s methodology represents a proof of concept for a novel way of performing metaphor analysis. It does so by applying the method to one particular domain of social interest, namely the gun debate in American political discourse. Though well aware that such an approach cannot replace a thorough cognitive, sociological, and political analysis, this paper offers examples that show how a cascade theory of metaphor and grammar helps automated data analysis in many ways.