Memes are the cultural equivalent of genes: units that transfer ideas or practices from one human to another by means of imitation. In recent years, network scientists have become increasingly interested in how memes spread.

This kind of work has led to important insights into the nature of news cycles, into information avalanches on social networks and into the role that networks themselves play in this spreading process.

But what exactly makes a meme and distinguishes it from other forms of information is not well understood. Today, Tobias Kuhn at ETH Zurich in Switzerland and a couple of pals say they’ve developed a way to automatically distinguish scientific memes from other forms of information for the first time. And they’ve used this technique to find the most important ideas in physics and how they’ve evolved in the last 100 years.

The word ‘meme’ was coined by the evolutionary biologists Richard Dawkins in his 1976 book The Selfish Gene. He argued that ideas, melodies, behaviours and so on, all evolve in the same way as genes, by means of replication and mutation, but using human culture rather than biology as the medium of evolution.

The process of replication—the transmission of information from one generation to the next— is crucial in evolution. Kuhn and pals point out that the world of scientific publishing is an ecosystem in which this process can be tracked in fine-grained detail.

That’s because scientific papers cite the papers on which they are based. This citation network makes it straightforward to determine whether a specific word or phrase has been transmitted from one paper to the next.

Kuhn and co’s mechanism for finding memes is based on exactly this. They mine the citation network of scientific papers looking for common words and phrases that appear often. Clearly, more common words must be more important.

But this alone does not distinguish memes from other types of information. The key feature of a meme is the way it replicates. So Kuhn and co define an interesting meme as one that is more likely to appear in a paper that cites another paper in which the same meme occurs. In other words, interesting memes are more likely to replicate.

That’s crucial because it provides a way to distinguish memes from other forms of information that do not spread in the same way through replication.

To test this approach, Kuhn and co apply this technique to the half a million papers published in Physical Review journals between 1893 and 2010.

The top 20 words and phrases identified in this way are:

1. loop quantum cosmology

2. unparticle

3. sonoluminescence

4. MgB2

5. stochastic resonance

6. carbon nanotubes

7. NbSe3

8. black hole

9. nanotubes

10. lattice Boltzmann

11. dark energy

12. Rashba

13. CuGeO3

14. strange nonchaotic

15. in NbSe3

16. spin Hall

17. elliptic ﬂow

18. quantum Hall

19. CeCoIn5

20. inﬂation

Most items on this list are noun phrases that accurately describe active and important areas of physics. That’s interesting because there is no filtering that excludes certain words and the algorithm has no knowledge of linguistics that allows it to pick out certain types of phrases.

To test whether these phrases are indeed interesting topics in physics, Kuhn and co asked a number of experts to pick out those that were interesting. The only ones they did not choose were: 12. Rashba, 14. ‘strange nonchaotic’ and 15. ‘in NbSe3'.

Kuhn and co also checked Wikipedia, finding that about 40 per cent of these words and phrases have their own corresponding entries. Together this provides compelling evidence that the new method is indeed finding interesting and important ideas.

Having found the most important memes, Kuhn and co studied how they have evolved in the last hundred years or so. They say most seem to rise and fall in popularity very quickly. “As new scientiﬁc paradigms emerge, the old ones seem to quickly lose their appeal, and only a few memes manage to top the rankings over extended periods of time,” they say.

That’s interesting work that shows for the first time how to distinguish scientific memes from ordinary scientific information.

An important question is how this method might be used more broadly in science. Kuhn and co say they’ve already applied it to other areas of science in the form of the 47 million publications records in the Web of Science, PubMed Central as well as the American Physical Society.

However, they have not yet published the top memes from this work . We’d be interested to see the top memes in science when they’re ready to reveal them.

Ref: arxiv.org/abs/1404.3757 : Inheritance Patterns In Citation Networks Reveal Scientiﬁc Memes