The straw man effect: partisan misrepresentation in natural language

Abstract

Political discourse often seems divided not just by different preferences, but by entirely different representations of the debate. Are partisans able to accurately describe their opponents’ position, or do they instead generate unrepresentative “straw man” arguments? In this research we examined an (incentivized) political imitation game, by asking partisans on both sides of the US health care debate to describe the most common arguments for and against ObamaCare. We used natural language processing algorithms to benchmark the biases and blind spots of our participants. Overall, partisans showed a limited ability to simulate their opponents’ perspective, or to distinguish genuine from imitation arguments. In general, imitations were less extreme than their genuine counterparts. Individual difference analyses suggest that political sophistication only improves the representations of one's own side, but not of an opponents' side, exacerbating the straw man effect. Our findings suggest that false beliefs about partisan opponents may be pervasive.