Reader Murphy pointed me to the following infographic developed by Altmetric to explain their analytics of citations of journal papers. These metrics are alternative in that they arise from non-academic media sources, such as news outlets, blogs, twitter, and reddit.
The key graphic is the petal diagram with a number in the middle.
I have a hard time thinking of this object as “data visualization”. Data visualization should visualize the data. Here, the connection between the data and the visual design is tenuous.
There are eight petals arranged around the circle. The legend below the diagram maps the color of each petal to a source of data. Red, for example, represents mentions in news outlets, and green represents mentions in videos.
Each petal is the same size, even though the counts given below differ. So, the petals are like a duplicative legend.
The order of the colors around the circle does not align with its order in the table below, for a mysterious reason.
Then comes another puzzle. The bluish-gray petal appears three times in the diagram. This color is mapped to tweets. Does the number of petals represent the much higher counts of tweets compared to other mentions?
To confirm, I pulled up the graphic for a different paper.
Here, each petal has a different color. Eight petals, eight colors. The count of tweets is still much larger than the frequencies of the other sources. So, the rule of construction appears to be one petal for each relevant data source, and if the total number of data sources fall below eight, then let Twitter claim all the unclaimed petals.
A third sample paper confirms this rule:
None of the places we were hoping to find data – size of petals, color of petals, number of petals – actually contain any data. Anything the reader wants to learn can be directly read. The “score” that reflects the aggregate “importance” of the corresponding paper is found at the center of the circle. The legend provides the raw data.
Some years ago, one of my NYU students worked on a project relating to paper citations. He eventually presented the work at a conference. I featured it previously.
Notice how the visual design provides context for interpretation – by placing each paper/researcher among its peers, and by using a relative scale (percentiles).
I’m ignoring the D corner of the Trifecta Checkup in this post. For any visualization to be meaningful, the data must be meaningful. The type of counting used by Altmetric treats every tweet, every mention, etc. as a tally, making everything worth the same. A mention on CNN counts as much as a mention by a pseudonymous redditor. A pan is the same as a rave. Let’s not forget the fake data menace (link), which affects all performance metrics.