Part II of series, that started by looking at GIScience (my core interest)
This is, as far as I know, the first publication of temporal trends in Google Metrics.
These Metrics provide an insight into the most successful/impactful publication outlets for individual disciplines (and subdisciplines) and also allow one to explore the most cited papers by their h5 index (h5 for a venue/author is the n (number) of papers with at least n citations, in this case for a 5 year period).
There are problems with the way these data are collected (not all venues are monitored, and the coverage may not be 100%, see here). The coverage has been slowly improving over the years. While Computer Science is relatively well covered, some conferences/workshop published in the well respected Springer Lecture Notes in Computer Science series are not monitored by Google and the individual volumes can not be well sorted into disciplines anyway.
Anyway, this project has been running for 3 years now and we can start looking at some trends (without any statistical insights, for this the series are too short). It is worth to note some separation of the journals into tears ( purely visually). Note that this may not say anything about the quality of the venue itself but maybe the audience is smaller/more niche).
It would be worth to compare these trends with the sibling disciplijne of data mining/knowledge discovery, where many venues are used by both communities.
Also note the discussion of the h5 index in here (Vrettas and Sanderson, 2015), suggesting that the size of the venue tends to lead to an over-inflation of its h5index. I would be happy to include additional venues into this, and share data for deeper investigation. I acknowledge the seed list of IR venues from @IR_oldie for this analysis.
I am looking forward to comments!
Vrettas, G. and Sanderson, M. (2015), Conferences versus journals in computer science. Journal of the Association for Information Science and Technology. doi: 10.1002/asi.23349
The R Hadleyverse for rvest, tidyr, stringr, dplyr and ggvis! Great little problem to learn these!