According to one classic definition, framing is the use of language to "select some aspects of a perceived reality and make them more salient in a communicating text". It's a familiar notion in political science -- when politicians do it deliberately and egregiously, we call it "spin" -- but I would argue that it is also a fundamental property of linguistic communication in any setting. In this talk I'll discuss work on computational modeling of framing, primarily using extensions of Bayesian topic models, as a way of studying the connection between signals in language use and underlying mental state. This has applications not only in political science, where underlying mental state includes notions like party self-identification or ideological bias, but in mental health, where underlying mental state can include conditions such as clinical depression.