A Proposal for a New #Altmetric, the Influence Score, to Accompany the H-Index and to Help Evaluate a Scientist’s Impact on Society

The H-index is among the most widely used metrics for evaluating the quality and quantity of a scientist’s publications; but what about their influence on society? Here I introduce the Influence Score that can help an outside reviewer better understand a person’s impact not just among other scientists but among the general public and those outside of academia. If the ultimate goal is to evaluate a person’s true overall role as a scientist, I think we should be considering how they communicate with all people not just other scientists (which is the case with the H-index). The new index can be used to accompany the H-index, but also incorporates it. All elements of the Influence Score can be looked up through simple Google and Twitter/Facebook searches.

            The H-index can be easily calculated in Google Scholar, I prefer it to the Web of Science or Scopus because Google Scholar counts books and other

Fig.1. Google Scholar Profile Page Showing H-Index in Red Box.

non-traditional peer-reviewed publications: and it is free! To calculate the H-index you essentially count down the number of publications and their citations until the numbers no longer overlap. A person with an H-index of 5, has at least 5 papers with 5 citations. Read more HERE to learn how to calculate the H-index.) Because a scientist’s main role is still to communicate their science among peers (via peer review), the Influence Score multiples their H-index by 100, and down weights the other elements, which are a little easier to accumulate (e.g., # of twitter followers).  I chose the H-index over say, total number of citations (which might be more similar to # of followers), because it is easier to calculate for a given researcher, especially one without a Google Scholar profile (Fig.1). 

            The other measures of the Influence score includes a measure of their visibility with the press (i.e, the Press Index or P-Index for short). Using Google News, one simple puts the person’s name in the search box and counts the number of articles that are found, which Google also does for you (Fig.2). Because

Fig 2. Red box shows Google News P-index.

Google News is only searching through a relatively recent window of time, few scientists will have much more than a few articles about them. Sometimes it is worth googling the person with “science” following their name, as I did for James Watson (e.g., “James Watson science”), to distinguish him from other news articles about people with the same name. This measure largely is to bump up those scientists truly making a social impact as newsmakers. That is without bumping them up too much, I’m trying to avoid giving too much influence to “celebrity scientists” that don’t do much science of their own. Therefore, I suggest that you divide the total number of search results of the Press Index by 100 so that this score is not completely overweighing the person’s academic accomplishments represented by the (albeit crude) measure of the H-index. Folks like James Cameron that are great promoters of science, but are best known for other things, are intentionally excluded here. If someone could separate press about science related activities from all others, they obviously could still be included. This is also the most dynamic element of the score because it can change so rapidly. Jane Goodall can skyrocket to the top of the list with the publication of a new book.

Fig.3. Twitter and Facebook Fan pages showing # of followers.

            The third part of the Influence Score considers your sway in social media (i.e, the Social Media Index or SM-index for short), specifically Twitter or Facebook. For someone on Twitter you get 1pt for every follower. For someone not on Twitter but that has either a Facebook “Fan” Page or Facebook “followers,” you get 1pt per fan or follower. You don’t get points for regular old Facebook “friends” because that isn’t necessarily measuring your scientific influence. If they have both a Facebook Fan page and a Twitter handle you only get points for whichever is the higher value. To learn more about the role of twitter for outreach read David Shiffman’s @whysharksmatter excellent article HERE).  As with the Press-Index you divide the total number by 100; again this is to allow the more academic H-index to still have some weight. The reason being that someone with 40,000 Twitter followers and an H-index of 0 might not really be more influential than a scientist with an H-index of 40 and only 4000 twitter followers.

            The Influence Score is then the total of your (H-Index X 100) + (Press index/100) + (Social Media-Index/100). I would round this to the nearest integer. All three can be discovered relatively easily through searches (e.g. GoogleScholar, GoogleNews, and a Facebook/Twitter search). Below I’ve compiled a list of some of the most well known scientists and have calculated their Influence Score. I’ve also added folks randomly that I admire that might not be the most famous folks but that I hope will be one day, I think adopting the Influence Score might help them get the recognition they deserve for the impact they have in society. This metric is imperfect: but I hope it is a good start.

I would like to thank Paige Brown @FromTheLabBench and her class #manship4002 for helping me figure out a more user-friendly way to compile this Influence Score. Also would like to thank Joshua Drew (@Drew_Lab) and David Shiffman (@WhySharksMatter) for their comments and advice.

Scientist	H-Index x 100	Press Index /100 (total articles according to GoogleNews)	Social Media Index /100 (Twitter/or Facebook followers)	Influence Score
Stephen Hawking	8400	781	9,591	18,772
Neil deGrasse Tyson	600	14	16,100	16,714
Richard Dawkins	4000	36	8,870	12,906
Bill Nye	100	140	11,500	11,740
Jared Diamond	10800	104	208	11,112
J. Craig Venter	9600	20	186	9,806
E.O. Wilson	8800	64	12	8,876
Steven Chu	8200	17	212	8,429
Buzz Aldrin	0	10	8,150	8,160
Sean B. Carroll	7500	.01	0	7,500
Jane Goodall	4500	21	1,650	6,171
Ed Yong	0	.12	4,660	4,660
Jane Lubchenco	4600	.71	0	4,601
Jack Horner	3600	.16	18	3,618
Carl Zimmer	1700	3.83	1700	3,404
Amanda Vincent	3200	0	3	3,203
Neil Shubin	3100	3.94	9	3,113
David Attenborough	1500	45	1,423	2,968
James Watson	2600	18	0	2,618
Hope Jahren	2200	0.01	24	2,224
Eugenie Clark	2200	0.01	0	2,200
Eugenie Scott	1800	0.06	71	1,871
David Shiffman	200	0.04	1,470	1,670
Sylvia Earle	1400	1	213	1,614

Note: As always I would like feedback on this post and if people have suggestions for changes or additions to the metric. Please e-mail me at prosanta@lsu.edu