What happens when we begin to think of all information as data that can be explored to yield new insights into our world? What would it look like to take nearly a decade of CNN, Fox News, and MSNBC television broadcasts and two years of BBC News broadcasts and run them through sophisticated natural language processing algorithms to identify every mention of a location on earth in their coverage and then create a series of maps that visualize the places we hear about when we turn to the news? What would those maps look like and what might they tell us about what we see when we turn on our televisions each day? Half a decade ago I began working with the Internet Archive's incredible Television News Archive to explore how powerful computer algorithms could allow us to "see" the news in entirely new ways. From simple longitudinal keyword searches to mass emotion mining to geographic mapping to the most powerful deep learning algorithms watching political ads, television has an incredible amount to teach us as we explore it through the modalities and lenses of massive data mining.

Hard news articles, just like op-ed articles, can reflect a media organization's bias. This study assesses bias in the hard news articles published by Fox News and MSNBC. Indicative linguistic features identified by the Gramulator reveal biases in corpora from the two networks.