How the WSJ is using machine learning to identify gaps in coverage
Francesco Marconi, head of research and development at The Wall Street Journal, writes about how the newspaper is using machine learning methods to find gaps in editorial coverage.
Marconi writes, “The results of our cluster analysis showcase the benefits of using machine learning as an input in our strategy:
- The coverage map was able to cluster over 20,000 Journal articles into 361 hyper-granular clusters with more nuance than traditional metadata allows. The articles contained in these clusters are all semantically similar — that is, they use similar words in similar ways. Looking through the ‘heat map’, journalists can see very specific clusters about, for example, US monetary policy, the US-China tech war, immigration in Europe, personal finance related to healthcare or green energy markets.
- Looking at clusters with high conversion rates, meaning topics that prompted a lot of readers to subscribe to the Journal, help us identify coverage opportunities that can impact new readership and drive engagement.
- By pairing the resulting granular topic information with additional metrics like story length and scroll depth, we can discover which areas of coverage our readers are responding to best. This, in turn, allows us to develop content strategy briefs for different sections of the Journal that align with the information needs of people who read our publication.”
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