Alexander Nwala

Grant C. Atkins, Alexander Nwala, Michele C. Weigle et al.

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.

Alexander Nwala, Michael Nelson

General purpose Search Engines (SEs) crawl all domains (e.g., Sports, News, Entertainment) of the Web, but sometimes the informational need of a query is restricted to a particular domain (e.g., Medical). We leverage the work of SEs as part of our effort to route domain specific queries to local Digital Libraries (DLs). SEs are often used even if they are not the "best" source for certain types of queries. Rather than tell users to "use this DL for this kind of query", we intend to automatically detect when a query could be better served by a local DL (such as a private, access-controlled DL that is not crawlable via SEs). This is not an easy task because Web queries are short, ambiguous, and there is lack of quality labeled training data (or it is expensive to create). To detect queries that should be routed to local, specialized DLs, we first send the queries to Google and then examine the features in the resulting Search Engine Result Pages (SERPs), and then classify the query as belonging to either the scholar or non-scholar domain. Using 400,000 AOL queries for the non-scholar domain and 400,000 queries from the NASA Technical Report Server (NTRS) for the scholar domain, our classifier achieved a precision of 0.809 and F-measure of 0.805.