Measuring News Similarity Across Ten U.S. News Sites
This provides a method for quantifying news similarity, which is incremental as it applies existing techniques to a known but poorly measured phenomenon in media studies.
The paper tackled the problem of measuring similarity in top news stories across ten U.S. news websites by developing a parser to extract stories and using cosine similarity, finding that similarity scores varied around the 2016 presidential election, with values like 0.335 before, 0.328 on, and 0.354 after Election Day.
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.