4 New things about Google Scholar – UI, recommendations, and citation networks

This post was originally published on this site

I’m actually a pretty big fan of Google Scholar, which in some ways is better than our library discovery service ,but even if you aren’t a fan, given it’s popularity it’s important for librarians to keep up with the latest developments.

In any case, I’m happy to see that Google continues to enhance Google Scholar with new features. These are some of the new features and things I’ve learnt about Google Scholar lately.


1. Google Scholar’s new UI

The new interface is a lot cleaner, particularly when on mobile and most of the changes aren’t really major (e.g. replacing text of “save” and “cite” with icons) but I miss the easy access to advanced search the old interface had.
Click the down arrow button to get access to advanced search in the old Google Scholar
In new Google Scholar interface, it now tucked under the “ham burger menu”, where more people might notice it. 
On the plus side, very few people knew Google Scholar had a advanced search or even how to access it , so overall it might be a ok trade-off, though it takes two clicks instead of one to access the advanced search.
Also the change to Google Scholar doesn’t seem to have affected link resolvers, various extensions that rely on Google Scholar via scraping such as Publish or Perish , Google Scholar button, so this is still a relatively minor layout change.

2.  Get recommendations of related works of other scholars’ works

Official change announcement. 

For a long time Google Scholar had a odd gap. As arguably the largest scholarly index in the world, with perhaps the largest number of users of any scholarly search engine, it was well posed to use all this data to create a fantastic recommendation system. Add Google’s famed machine learning and it looked like a no-brainer.

But it was only in 2012, nearly 8 years after launch that Google Scholar added a recommendation system. And as you might expect, the recommendations are excellent. While other recommendation systems for scholarly material exist (e.g. BX recommender, Mendeley’s, various publisher based ones), none in my opinion are as broad ranging or timely as Google Scholar for the reasons already mentioned.


Google Scholar recommended articles

Still there was a curious gap. The recommendation system only gave recommendations based on the works already in your Google Scholar profile.

The flaw here is obvious, what if you were working in a new area you haven’t published formally yet? Arguably this is exactly when you have a greater need of the help of a recommendation system.

I wanted a feature where I could put a set of articles into Google Scholar and it would give recommended articles over time. One crazy idea I had back at the time was to create a brand new fake Google Scholar profile , load it up with works of articles I’m interested in , keep the profile private and leverage on the recommendations provided.

Unfortunately this doesn’t work, because the Google Scholar profile has to be public before recommendations appear.

Another way that probably works is to exploit the fact that papers deposited into ResearchGate, preprint servers do appear in Google Scholar and hence can be added to your profile fairly quickly. So you could example, create a quick working paper (with citations to works you know of) and deposit it in a institutional repository or preprint server that is indexed by Google Scholar. Add those into your Google Scholar profile and wait for recommendations to appear. But this still seems really forced and do you really want to mess up your profile just to get a few recommendations?

So the new feature added by Google is much appreciated. While you still cant add any arbitrary set of articles, you can go to any Scholarly profile and choose to follow the author’s new works, citiations and most importantly articles related to the author’s research.


Follow Harzing profile to get recommended articles similar to her research publications in Google Scholar

It’s not super clear to me if it just sends new articles via email or whether it updates the recommended articles list you get within Google Scholar, I suspect the former and technically articles shown this way are alerts not recommendations?, but it’s still useful.

3. Google citation profile improvements – allows one time export to ORCID

This isn’t a new feature in Google Scholar but fairly new feature in ORCID.
I often find many researchers have their Google Scholar profile fully filled up with their works (no doubt partly because Google makes it so easy , particularly with auto or semi-auto updates and partly becuase they reason the profile increases their visibility), but are reluctant to spend the time to get their ORCID profile populated.
Exporting works via BibTex
This of course only works as a one-time upload and you would have to continue to update future works, hopefully by other automated ways (e.g. via Journal crossref links, from CRIS/RIMs etc).
Another fairly new feature in Google Scholar citations is that they now try to group together authors by institutions. So for example when you search for the name of an institution in Google Scholar, you get something like this.
Searching by institutions in Google Scholar
Clicking on the link gets you this.
Top cited profiles from the institution
There’s a study on how accurate this institution matching is  but what are the practical implications for normal librarians who aren’t doing advanced bibliometrics?
For one it allows you to fairly easy get top 10, 20 etc cited authors of your institution, to complement the lists you get from Web of Science/incites or Scopus/Scival.
You can’t jump to the end of results to gauge how many authors your institution has on this platform. 
It’s unfortunate that for this set of results, Google doesn’t list the number of results, and neither can you gauge it by looking at the number of pages in the results list  and you can only go forward page by page(see above).
I don’t know of a way around it , even if you alter the url parameters “&after_author “or “&astart=30” it doesn’t work.


4. Scrapping of Google Scholar to create network diagrams/ Bibliometric networks

It basically works as follows. First the system allows you to search via Google Scholar for papers to add.
Below I searched the term Open access, and then added some of the papers into the system. You can of course search for specific papers by title.
Once you are done with a set of papers, you can click on “Check Citations” and it will use Google Scholar’s “search within citing articles” feature to see if the articles in your set of papers are connected.
It took me a while to understand how it worked but here’s a specific example.
Say you have the following two papers
“Eysenbach, G. (2006). Citation advantage of open access articles. PLoS biology, 4(5), e157.”
“Harnad, S., & Brody, T. (2004). Comparing the impact of open access (OA) vs. non-OA articles in the same journals. D-lib Magazine, 10(6).”
The system will automatically go to say the Google Scholar list of citations for Harnad, S., & Brody, T. (2004) and using the “Search within citing articles” check to see if G. Eysenbach, is included.
It will do this for all pairs of authors in your set of reference articles automatically. All these searches are done in a popup window, if the volume is too big , Google Scholar will throw up a captcha for you to solve and it will then continue.
You can then export a basic visualization of the author network which shows coauthorships and citations. Here’s my first toy example, using papers I cited in a recent working paper.
It isn’t too impressive probably because I don’t have enough papers but it does show the structure I expected with 2 main clusters – one around LC smith (1981) (old paper on citation analysis for library collection evaluation) and one around Eysenbach (2006) , a well cited early paper on citation advantage. I would have thought they would not be connected at all (particularly since I remove Eugene Garfield’s seminal publications) but they still seem to be linked indirectly.though an author who cited both.
You can export the network for further study into the open source Gephi network visualization tool and I have spent some time doing so playing with more complicated networks like publication & author networks, using modularity to identify clusters of works. I’ll probably blog about this in a separate blog post next time, but for now I’m very intrigued.

How useful are such networks for researchers?

Could doing such network graphs be useful for researchers, particularly those new to the field to help them see how their research fits into existing research, and see connections they wouldn’t otherwise have seen?
Could this be autogenerated from references of existing papers to help the reader get a sense of where the current piece of article sits?
Can such network graphs provide improved recommendations (or does recommendations from Google Scholar etc already implicitly do that?)
How big a network (or set of articles) is needed before this becomes useful? e.g. Is this useful only for thesis with over 50 references (or better yet include everything in your reference manager not just what you cited)? Would most researchers find that these network graphs only reproduce clusters they already intuitively know or provide some unexpected insights?
In a future post I will talk about my experiments on these 3 scenarios
a) Visualizing networks between publications that cite my old 2009 article
b) Visualizing networks between publications cited in my old 2009 article and newest paper
c) Visualizing networks between publications cited in an article not in my field. (to see if it helps orientate me better in an area I’m not familiar with).
Would I learn anything from doing such visualizations?
Of course this idea isn’t new, I’m guessing there should be research out there on this.
Existing tools like web of science have limited citation map capabilities and the newer incites and scival also provide mapping capabilities though often at the higher level meant for research administrators.

On the free side, VOSviewer also provides the ability to visualization networks of citations. The newest version 1.6.6  actually adds the ability to generate networks not just from Scopus and Web of Science citations but also from Crossref.



VOSviewer 1.6.6 supports Web of Science, Scopus, PubMed and Crossref 


So one can generate similar networks using dois from VOSviewer. Still I suspect scraping from Google Scholar might give richer results due to the much large scale of Google Scholar compared to say Scopus. Also given the popularity of Google Scholar has a discovery tool, one might find relying on other tools such as Scopus to create networks might risk missing too many works found via Google Scholar.


Hope you found some of this useful.

It’s good to see Google continue to improve Google Scholar, while we may not know when Google might decide to abandon Google Scholar , the recent spate of improvements are a good sign it won’t  be anytime soon.

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