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The Journal lmpact Factor (JIF) was first proposed by Eugene Garfield of Institute for Scientific Information (ISI) fame in 1955. It is a journal specific yearly citation measure, defined to be the average number of citations per paper of the papers published in the preceding two years. Obsession with the impact factor in the face of widespread recognition of its shortcomings as a tool for judging the value of science is an unfortunate example of “the tragedy of the commons”.
Leaving aside for a moment the flaws of the JIF, one may wonder whether journals do in fact have any impact? By “impact”one might imagine something along the lines of the simple definition in the Merriam-Webster Dictionary: “to have a strong and often bad effect on (something or someone)” and as an object for the impact one could study the researchers who publish, the scientific community as a whole, or the papers themselves. On the question of impact on papers, common sense suggests that publishing in a high profile journal helps a paper succeed and there is pseudoscience to support that case. However there is little in the way of direct measurement. Twitter to the rescue.
At the end of last year my twitter account was approaching 5,000 followers. Inspired by others, I found myself reflecting on this “milestone” and in anticipation of the event, I started to ponder the scientific utility of amassing such a large numbers of followers. There is, of course, a lot of work being done on natural language processing of twitter feeds, but it struck me that with 5,000 followers I was in a position to use twitter for proactive experimentation rather than just passive mining. Impact factors, followers, and twitter… it was just the right mix for a little experiment…
In my early tweeting days I encountered a minor technical issue with links to papers: it was unclear to me whether I should use link shorteners (and if so which service?) or include direct links to articles in my tweets. I initially thought that using link shorteners would save me characters but I quickly discovered that this was not the case. Eventually, following advice from fellow twitterati, I began tweeting articles only with direct links to the journal websites. Last year, when twitter launched free analytics for all registered users, I started occasionally examining the stats for article tweets, and I began to notice quantitatively what I had always suspected intuitively: tweets of Cell, Nature and Science (CNS) articles were being circulated much more widely than those of other journals. Having use bit.ly, the natural question to ask was how do tweets of journal articles with the journal names compare to tweets with anonymized links?
Starting in August of 2015, I began occasionally tweeting articles about 5 minutes apart, using the exact same text (the article title or brief description) but doing it once with the article linked via the journal website so that the journal name was displayed in the link and once with an a bit.ly link that revealed nothing about the journal source. Twitter analytics allowed me to see, for each tweet, a number of (highly correlated) tweet statistics, and I settled on measuring the number of clicks on the link embedded in the tweet. By switching the order of named/anonymized tweets I figured I could control for a temporal effect in tweet appearance, e.g. it seemed likely that users would click on the most recent links on their feed resulting in more views/clicks etc. for later tweets identical except for link type . Ideally this control would have been performed by A/B testing but that was not a possibility (see Supplementary Materials and Notes). I did my tweeting manually, generally waiting a few weeks between batches of tweets so that nobody would catch on to what I was doing (and thereby ruin the experiment). I was eventually caught forcing me to end the experiment
but not before I squeezed in enough tests to achieve a significant p-value for something.
I hypothesized that twitter users will click on articles when, and only when, the titles or topics reflect research of interest to them. Thus, I expected not to find a difference in analytics between tweets made with journal names as opposed to bit.ly links. Strikingly, tweets of articles from Cell, Nature and Science journals (CNS) all resulted in higher clicks on the journal title rather than the anonymized link (p-value 0.0078). The average effect was a ratio of 2.166 between clicks on links with the journal name in comparison to clicks on bit.ly links. I would say that this number is the real journal impact factor of what are now called the “glamour journals” (I’ve reported it to three decimal digits to be consistent with the practice of most journals in advertising their JIFs). To avoid confusion with the standard JIF, I call my measured impact factor the RIF (relative impact factor).
One possible objection to the results reported above is that perhaps the RIF reflects an aversion to clicking on bit.ly links, rather than a preference for clicking on (glamour) journal links. I decided to test that by performing the same test (journal link vs. bit.ly link) with PLoS One articles:
Strikingly, in three out of the four cases tested users displayed an aversion to clicking on PLoS One links. Does this mean that publishing in PLoS One is career suicide? Certainly not (I note that I have published PLoS One papers that I am very proud of, e.g. Disordered Microbial Communities in Asthmatic Airways), but the PLoS One RIF of 0.877 that I measured (average ratio of journal:bit.ly clicks, as explained above) is certainly not very encouraging for those who hope for science to be journal name blind. It also suggests that the RIF of glamour journals does not reflect an aversion to clicking on bit.ly links, but rather an affinity for.. what else to call it but.. glamour.
Academics frequently complain that administrators are at fault for driving researchers to emphasize JIFs, but at the recent Gaming Metrics meeting I attend UC Davis University Librarian MacKenzie Smith pointed out something which my little experiment confirms: “It’s you!”
Supplementary Material and Notes
The journal Nature Communications is not obviously a “glamour journal”, however I included it in that category because the journal link name began nature.com/… Removing the Nature Communications tweet from the glamour analysis increases the glamour journal RIF to 2.264.
The ideal platform for my experiment is an A/B testing setup, and as my former coauthor Dmitry Ryaboy , head of the experimentation team at twitter explains in a blog post, twitter does perform such testing on users for internal purposes. However I could not perform A/B testing directly from my account, hence the implementation of the design described above.
I tried to tweet the journal/bit.ly tweets exactly 5 minutes apart, but once or twice I got distracted reading nonsense on twitter and was delayed by a bit. Perhaps if I’d been more diligent (and been better at dragging out the experiment) I’d have gotten more and better data. I am comforted by the fact that my sample size was >1.
Twitter analytics provided multiple measures, e.g. number of retweets, impressions, total engagements etc., but I settled on link clicks because that data type gave the best results for the argument I wanted to make. The table with the full dataset is available for download from here (or in pdf). The full list of tweets is here.