As Internet users increasingly rely on social media sites like Facebook and Twitter to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. Inferring the bias (or slant) of these media pages poses a difficult challenge for media watchdog organizations that traditionally rely on content analysis. In this paper, we explore a novel scalable methodology to accurately infer the biases of thousands of news sources on social media sites like Facebook and Twitter. Our key idea is to utilize their advertiser interfaces, that offer detailed insights into the demographics of the news source’s audience on the social media site. We show that the ideological (liberal or conservative) leaning of a news source can be accurately estimated by the extent to which liberals or conservatives are over-/under-represented among its audience. Additionally, we show how biases in a news source’s audience demographics, along the lines of race, gender, age, national identity, and income, can be used to infer more fine-grained biases of the source, such as social vs. economic vs. nationalistic conservatism. Finally, we demonstrate the scalability of our approach by building and publicly deploying a system, called "Media Bias Monitor", which makes the biases in audience demographics for over 20,000 news outlets on Facebook transparent to any Internet user.