  News outlets are a primary source for many people to learn what is going on
in the world. However, outlets with different political slants, when talking
about the same news story, usually emphasize various aspects and choose their
language framing differently. This framing implicitly shows their biases and
also affects the reader's opinion and understanding. Therefore, understanding
the framing in the news stories is fundamental for realizing what kind of view
the writer is conveying with each news story. In this paper, we describe
methods for characterizing moral frames in the news. We capture the frames
based on the Moral Foundation Theory. This theory is a psychological concept
which explains how every kind of morality and opinion can be summarized and
presented with five main dimensions. We propose an unsupervised method that
extracts the framing Bias and the framing Intensity without any external
framing annotations provided. We validate the performance on an annotated
twitter dataset and then use it to quantify the framing bias and partisanship
of news.
