  News websites make editorial decisions about what stories to include on their
website homepages and what stories to emphasize (e.g., large font size for main
story). The emphasized stories on a news website are often highly similar to
many other news websites (e.g, a terrorist event story). The selective emphasis
of a top news story and the similarity of news across different news
organizations are well-known phenomena but not well-measured. We provide a
method for identifying the top news story for a select set of U.S.-based news
websites and then quantify the similarity across them. To achieve this, we
first developed a headline and link extractor that parses select websites, and
then examined ten United States based news website homepages during a three
month period, November 2016 to January 2017. Using archived copies, retrieved
from the Internet Archive (IA), we discuss the methods and difficulties for
parsing these websites, and how events such as a presidential election can lead
news websites to alter their document representation just for these events. We
use our parser to extract k = 1, 3, 10 maximum number of stories for each news
site. Second, we used the cosine similarity measure to calculate news
similarity at 8PM Eastern Time for each day in the three months. The similarity
scores show a buildup (0.335) before Election Day, with a declining value
(0.328) on Election Day, and an increase (0.354) after Election Day. Our method
shows that we can effectively identity top stories and quantify news
similarity.
