IMPACT or How I Learned to Start Worrying and Fear Altmetrics

Altmetrics is the idea that scientific publications should be judged (perhaps primarily) on the impact they have in the general media, including on social media. This is in alternative to looking at either citations of journal impact factors.


People who know me outside of science know that antibiotic overuse is a pet peeve of mine [1]. We just published a paper touching on this very subject. It also touched on antibiotic use in agriculture. Both of these can be sold as hot subjects and it’d certainly be possible to try to get some attention in social media with a few bolder statements: antibiotic use in factory farming causes antibiotic resistant infections!

Oh, the altmetrics would go through the roof, but we don’t have the data to support anything like that claim. Our data and analysis is congruent with the idea that antibiotic overuse by humans and farm animals leads to increased resistance which may lead to increased antibiotic resistant infections, but we must acknowledge that there are a large number of confounders and no proof of direct causality. Broadly speaking, people in countries that like to give their animals antibiotics also take a bunch themselves, thus we cannot disentangle farm-to-fork from human antibiotic (over)use. Furthermore, the presence of antibiotic resistant genes is not sufficient to infer the presence of clinically-relevant antibiotic resistant pathogens (this may be a limitation of current methods of analysis, naturally, but a limitation it is). The paper, naturally, has more details on these questions.

We wrote as good scientists, presenting our data and conclusions, acknowledging limitations. We hope to get scientific recognition for this. Most directly in the form of citations, naturally, but more generally in recognition (those people in the Bork lab did a really good job both on their own data and in reviewing other work).

If our incentives were to stir up controversy in social networks, then they would point away from this towards a more polemical stance (and whilst they may, in some sense, draw more engagement with scientific results, they would, in a more fundamental sense, move the discourse away from a evidence-based direction [2]).

When writing blogposts, I put in short pithy sentences for twitter; it’d be dangerous if I did the same when writing a journal paper.


Metrics don’t just measure, they also shape behaviour, you need to solve for the equilibrium.

You need to ask: would it be a good thing if people started, on the margin, to optimize for your metric? In the case of scientists and altmetrics, the answer may be NO.


An unrelated criticism of altmetrics is that they’d be outright gamed and that the scientific world has nowhere close to the capacity to fight spam like google et al. do. The linked article is also notable for using the word meretricious in the title.

Also, do read the rejoinder.

[1] I’m the sort of guy that when a person complains that their doctor didn’t give them antibiotics for the flu is liable to praise the doctor instead of expressing empathy.
[2] In fact, public diffusion of speculative scientific results can lead to mistrust of science as these speculative results will then tend to contradict themselves leading to dismissal of science in general.

Friday Links

1. A wonderful explanation of Bregman divergences. I also learned that cool result of Banerjee & Gou.

2. I liked The Enduring Myth of the SPARQL Endpoint. This reminded of Titus Brown’ remark that scaling is the only CS problem that still matters.

Money quote: There is a reason there are no ‘SQL Endpoints’.

3. Why proxy measures fail: this applies to Journal Impact Factor, but will also apply to altmetrics. In fact, we can read it as arguing that metrics that are not widely used look better than metrics that are widely used. But it is fallacious to expect that proposed metrics will not go through the same process.

4. President of the AAAS faked her PhD.

5. This battle will eventually reach biology. (This is a much better exposition of the anti-ML position than Chomsky’s rants, by the way).

I think it will be a while before we can just predict any biological behaviour that is interesting, though.

However, how soon before somebody claims that

Every time I fire a biologist, the performance of our systems biology model goes up.

Is the curious task of machine learning to demonstrate to men how little they really know about what they imagine they can study?