Augmenting Local Angles with Data
As local papers are forced to cut back on coverage and struggle to survive, the future of local news is uncertain. Some communities are even considered “news deserts” with little to no local information. What can be done to increase the volume of local news? The goal of this project is to address this issue by considering how national reporting can be augmented with a local angle based on data.
To prototype this idea students will do research to find datasets with national coverage (e.g. from the census) and consider how locally relevant facts can be derived from those datasets. The facts will then be turned into snippets of text using template-based natural language generation. These fact snippets will be built into an article plugin that inserts the locally relevant fact into a national article based on the location of the user (e.g. as reported by their browser). For instance, a New York Times article about the unemployment rate would have a snippet automatically inserted about the unemployment rate in your home county, including how it’s changed and how it varies by demographic.
Students will build a functioning prototype of an article page that is dynamically localized based on a user’s location using fact snippets automatically generated from data. They will gain experience working with geographic data and with natural language generation template writing.