When a website hosting user-generated content asks users a straightforward question - "Was this content helpful?" with one "Yes" and one "No" button as the two possible answers - one might expect to get a straightforward answer. In this paper, we explore how users respond to this question and find that their responses are not quite straight-forward after all. Using data from Amazon product reviews, we present evidence that users do not make absolute, independent voting decisions based on individual review quality alone. Rather, whether users vote at all, as well as the polarity of their vote for any given review, depends on the context in which they view it - reviews receive a larger overall number of votes when they are 'misranked', and the polarity of votes becomes more positive/negative when the review is ranked lower/higher than it deserves. We distill these empirical findings into a new probabilistic model of rating behavior that includes the dependence of rating decisions on context. Understanding and formally modeling voting behavior is crucial for designing learning mechanisms and algorithms for review ranking, and we conjecture that many of our findings also apply to user behavior in other online content-rating settings.