## Tips from the Blog II

An IgorPro tip this week. The default font for plots is Geneva. Most of our figures are assembled using Helvetica for labelling. The default font can be changed in Igor Graph Preferences, but Preferences need to be switched on in order to be implemented. Anyway, I always seem to end up with a mix of Geneva plots and Helevetica plots. This can be annoying as the fonts are pretty similar yet the spacing is different and this can affect the plot size. Here is a quick procedure Helvetica4All() to rectify this for all graph windows.

## You Know My Name (Look Up The Number)

This thought crossed my mind yesterday when I saw a tweet that was tagged #academicinsults

It occurred to me that a Twitter account is a kind of micro-publishing platform. So what would “publication metrics” look like for Twitter? Twitter makes analytics available, so they can easily be crunched. The main metrics are impressions and engagements per tweet. As I understand it, impressions are the number of times your tweet is served up to people in their feed (boosted by retweets). Engagements are when somebody clicks on the tweet (either a link or to see the thread or whatever). In publication terms, impressions would equate to people downloading your paper and engagements mean that they did something with it, like cite it. This means that a “h-index” for engagements can be calculated with these data.

For those that don’t know, the h-index for a scientist means that he/she has h papers that have been cited h or more times. The Twitter version would be a tweeter that has h tweets that were engaged with h or more times. My data is shown here:

My twitter h-index is currently 36. I have 36 tweets that have been engaged with 36 or more times.

So, this is a lot higher than my actual h-index, but obviously there are differences. Papers accrue citations as time goes by, but the information flow on Twitter is so fast that tweets don’t accumulate engagement over time. In that sense, the Twitter h-index is less sensitive to the time a user has been active on Twitter, versus the real h-index which is strongly affected by age of the scientist. Other differences include the fact that I have “published” thousands of tweets and only tens of papers. Also, whether or not more people read my tweets compared to my papers… This is not something I want to think too much about, but it would affect how many engagements it is possible to achieve.

The other thing I looked at was whether replying to somebody actually means more engagement. This would skew the Twitter h-index. I filtered tweets that started with an @ and found that this restricts who sees the tweet, but doesn’t necessarily mean more engagement. Replies make up a very small fraction of the h tweets.

I’ll leave it to somebody else to calculate the Impact Factor of Twitter. I suspect it is very low, given the sheer volume of tweets.

Please note this post is just for fun. Normal service will (probably) resume in the next post.

Edit: As pointed out in the comments this post is short on “Materials and Methods”. If you want to calculate your ownTwitter h-index, go here. When logged in to Twitter, the analytics page should present your data (it may take some time to populate this page after you first view it). A csv can be downloaded from the button on the top-right of the page. I imported this into IgorPro (as always) to generate the plots. The engagements data need to be sorted in descending order and then the h-index can be found by comparing the numbers with their ranked position.

The post title is from the quirky B-side to the Let It Be single by The Beatles.

## Vitamin K

Note: this is not a serious blog post.

Neil Hall’s think piece in Genome Biology on the Kardashian index (K-index) caused an online storm recently, spawning hashtags and outrage in not-so-equal measure. Despite all the vitriol that headed Neil’s way, very little of it concerned his use of Microsoft Excel to make his plot of Twitter followers vs total citations! Looking at the plot with the ellipse around a bunch of the points and also at the equations, I thought it might be worth double-checking Neil’s calculations.

In case you don’t know what this is about: the K-index is the ratio of actual Twitter followers ($$F_{a}$$) to the number of Twitter followers you are predicted to have ($$F_{c}$$) based on the total number of citations to your papers ($$C$$) from the equation:

$$F_{c}=43.3C^{0.32}$$

So the K-index is:

$$K-index=\frac{F_{a}}{F_{c}}$$

He argues that if a scientist has a K-index >5 then they are more famous for their twitterings than for their science. This was the most controversial aspect of the piece. It wasn’t clear whether he meant that highly cited scientists should get tweeting or that top-tweeters should try to generate some more citations (not as easy as it sounds). The equation for $$F_{c}$$ was a bit suspect, derived from some kind of fit through some of the points. Anyway, it seemed to me that the ellipse containing the Kardashians didn’t look right.

I generated the data for $$F_{c}$$ and for a line to show the threshold at which one becomes a Kardashian (k) in IgorPro as follows:

Make /o /N=100000 fc fc =43.3*(x^0.32) Duplicate fc k //yes, this does look rude k *=5 display fc, k //and again!

This plot could be resized and overlaid on Neil’s Excel chart from Genome Biology. I kept the points but deleted the rest and then made this graph.

The Kardashians are in the peach zone. You’ll notice one poor chap is classed as a Kardashian by Neil, yet he is innocent! Clearly below the line, i.e. K-index <5.

Two confessions:

1. My K-index today is 1.97 according to Twitter and Google Scholar.
2. Embarrassingly, I didn’t know of the business person who gave her name to the K-index was until reading Neil’s article and the ensuing discussion. So I did learn something from this!

The post title is taken from “Vitamin K” by Gruff Rhys from the Hotel Shampoo album.