Without further ado we’re excited to announce the first of our sketch projects: NYT Writes created by Irene Ros, a research developer in our lab. This sketch looks at The New York Times writers and the topics they write about. It compares the diversity of topics by authors to what a single topic is comprised of.

How does it work?
You begin by performing a search for a topic of interest. Pick a keyword you’re interested, such as “Tsunami”. This will fetch articles containing that term that were written in the last 30 days and build the visualization from them.
What am I looking at here?
There are a few things that you will see once the search is complete. First, on the left side of the screen you will see a stack of bubbles at varying sizes. Each bubble represents a term, or “facet”, that was used to describe one or more articles containing your search query. Facets get manually attached to each article by The New York Times staff. An article about “Tsunami” might be tagged as being about “Natural Disasters,” for example. The size corresponds to the relative amount of times that tag appeared comparing to all the other facets collected from all other articles in the query set. You can mouse over each bubble to see the tag name appear in the middle as well as how much it appeared relative to the other facets below the stack itself. This stack could also represent what I call a “dedicated writer” – someone who only writes about one topic for 30 days would have a similar stack to this one.
Hovering over a label also reveals its relationship to other facets through arcs. The white arcs that connect two bubbles indicate that the two facets were used at some point to describe the same article. The thicker the arc, the more times the pair was used across all articles in the query set.

What does the author list on the side mean?
As we collect articles into your query set, we also extract the authors that appear in the byline of the article. The list on the right side contains the authors we extracted alongside the number of articles they appeared on in the query set. They are sorted alphabetically. You can click an author’s name to get another facet bubble stack to appear beside your query bubble stack.
What does the author stack mean?
When you chose an author to review, we go ahead and fetch the articles they wrote in the last 30 days. We then collect the facets from those articles and build a bubble stack much in the same way we built the first one. The key here is that an author might (or might not) write about a diversity of topics and using the same visual structure we can compare how closely an author’s writing resembles the original query topic. In most cases, authors write about so many more topics than just the one we query for, showing us how versatile they have to be. The bubble sizes in the author stack are relative in size to those in the query stack. Mousing over a bubble in one stack will highlight that same facet bubble in the other stack.
When you’ve selected an author, you can see how closely they resemble the first bubble stack at the bottom of the visualization.

The similarity is computed via a standard metric called the Pearson Correlation Coefficient.
Where do I try it out live?
You can find it at http://nytwrites.thevcl.com.
Questions?
Feel free to contact me at imirene at gmail dot com.