Combining the complementary strengths of human and machine intelligence has the potential to shed new light on old questions in the social sciences. In this talk, I will demonstrate this potential by focusing on the applications of crowdsourcing and large scale content analysis techniques to two different problems. I will first present a recent work that investigates the selection and framing of political news at scale. Our study employs supervised learning methods to identify articles pertaining to political events in a large news corpus and next recruits online human judges to classify a representative sample of political articles according to topic and ideological position in a large randomized experiment. Such an analysis not only provides a reliable index of media bias but also allows us to investigate how this bias varies across different issues. Next, I will briefly discuss how human and machine intelligence can be employed to characterize charitable giving behavior and estimate the impact a widely adopted recommendation system could have in the charity marketplace.