Local Memory Project: Providing Tools to Build Collections of Stories for Local Events from Local Sources

Abstract

The national (non-local) news media has different priorities than the local news media. If one seeks to build a collection of stories about local events, the national news media may be insufficient, with the exception of local news which "bubbles" up to the national news media. If we rely exclusively on national media, or build collections exclusively on their reports, we could be late to the important milestones which precipitate major local events, thus, run the risk of losing important stories due to link rot and content drift. Consequently, it is important to consult local sources affected by local events. Our goal is to provide a suite of tools (beginning with two) under the umbrella of the Local Memory Project (LMP) to help users and small communities discover, collect, build, archive, and share collections of stories for important local events by leveraging local news sources. The first service (Geo) returns a list of local news sources (newspaper, TV and radio stations) in order of proximity to a user-supplied zip code. The second service (Local Stories Collection Generator) discovers, collects and archives a collection of news stories about a story or event represented by a user-supplied query and zip code pair. We evaluated 20 pairs of collections, Local (generated by our system) and non-Local, by measuring archival coverage, tweet index rate, temporal range, precision, and sub-collection overlap. Our experimental results showed Local and non-Local collections with archive rates of 0.63 and 0.83, respectively, and tweet index rates of 0.59 and 0.80, respectively. Local collections produced older stories than non-Local collections, at a higher precision (relevance) of 0.84 compared to a non-Local precision of 0.72. These results indicate that Local collections are less exposed, thus less popular than their non-Local counterpart.