Where’s the news?: Classifying News Domains

23 Jul

We select an initial universe of news outlets (i.e., web domains) via the Open Directory Project (ODP, dmoz.org), a collective of tens of thousands of editors who hand-label websites into a classification hierarchy. This gives 7,923 distinct domains labeled as: news, politics/news, politics/media, and regional/news. Since the vast majority of these news sites receive relatively little traffic, to simplify our analysis we restrict to the one hundred domains that attracted the largest number of unique visitors from our sample of toolbar users. This list of popular news sites includes every major national news source, well-known blogs and many regional dailies, and
collectively accounts for over 98% of page views of news sites in the full ODP list (as estimated via our toolbar sample). The complete list of 100 domains is given in the Appendix.

From Filter Bubbles, Echo Chambers, and Online News Consumption by Flaxman, Goel and Rao.

When using rich browsing data, scholars often rely on ad hoc lists of domains to estimate consumption of certain kind of media. Using these lists to estimate consumption raises three obvious concerns – 1) Even sites classified as ‘news sites,’ such as the NYT, carry a fair bit of non-news 2) (speaking categorically) There is the danger of ‘false positives’ 3) And (speaking categorically again) there is a danger of ‘false negatives.’

FGR address the first concern by exploiting the URL structure. They exploit the fact that the URL of NY Times story contains information about the section. (The classifier is assumed to be perfect. But likely isn’t. False positive and negative rates for this kind of classification can be estimated using raw article data.) This leaves us with concern about false positives and negatives at the domain level. Lists like those published by DMOZ appear to be curated well-enough to not contain too many false-positives. The real question is about how to calibrate false negatives. Here’s one procedure. Take a large random sample of the browsing data (at least 10,000 unique domain names). Compare it to a large comprehensive database like Shallalist. Of the domains that aren’t in the database, query a URL classification service such as Trusted Source. (The initial step of comparing against Shallalist is to reduce the amount of querying.) Using the results, estimate the proportion of missing domain names (the net number of missing domain names is likely much much larger). Also estimate missed visitation time, page views etc.
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