Archive for the ‘Meme’ Category

Inheritance Patterns in Citation Networks Reveal Scientific Memes

Sunday, December 14th, 2014

Inheritance Patterns in Citation Networks Reveal Scientific Memes by Tobias Kuhn, Matjaž Perc, and Dirk Helbing. (Phys. Rev. X 4, 041036 – Published 21 November 2014.)


Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Popular Summary:

It is widely known that certain cultural entities—known as “memes”—in a sense behave and evolve like genes, replicating by means of human imitation. A new scientific concept, for example, spreads and mutates when other scientists start using and refining the concept and cite it in their publications. Unlike genes, however, little is known about the characteristic properties of memes and their specific effects, despite their central importance in science and human culture in general. We show that memes in the form of words and phrases in scientific publications can be characterized and identified by a simple mathematical regularity.

We define a scientific meme as a short unit of text that is replicated in citing publications (“graphene” and “self-organized criticality” are two examples). We employ nearly 50 million digital publication records from the American Physical Society, PubMed Central, and the Web of Science in our analysis. To identify and characterize scientific memes, we define a meme score that consists of a propagation score—quantifying the degree to which a meme aligns with the citation graph—multiplied by the frequency of occurrence of the word or phrase. Our method does not require arbitrary thresholds or filters and does not depend on any linguistic or ontological knowledge. We show that the results of the meme score are consistent with expert opinion and align well with the scientific concepts described on Wikipedia. The top-ranking memes, furthermore, have interesting bursty time dynamics, illustrating that memes are continuously developing, propagating, and, in a sense, fighting for the attention of scientists.

Our results open up future research directions for studying memes in a comprehensive fashion, which could lead to new insights in fields as disparate as cultural evolution, innovation, information diffusion, and social media.

You definitely should grab the PDF version of this article for printing and a slow read.

From Section III Discussion:

We show that the meme score can be calculated exactly and exhaustively without the introduction of arbitrary thresholds or filters and without relying on any kind of linguistic or ontological knowledge. The method is fast and reliable, and it can be applied to massive databases.

Fair enough but “black,” “inflation,” and, “traffic flow,” all appear in the top fifty memes in physics. I don’t know that I would consider any of them to be “memes.”

There is much left to be discovered about memes. Such as who is good at propagating memes? Would not hurt if your research paper is the origin of a very popular meme.

I first saw this in a tweet by Max Fisher.

Meme Diffusion Through Mass Social Media

Tuesday, July 5th, 2011

Meme Diffusion Through Mass Social Media


The project is aimed at modeling the diffusion of information online and empirically discriminating among models of mechanisms driving the spread of memes. We explore why some ideas cause viral explosions while others are quickly forgotten. Our analysis goes beyond the traditional approach of applied epidemic diffusion processes and focuses on cascade size distributions and popularity time series in order to model the agents and processes driving the online diffusion of information, including: users and their topical interests, competition for user attention, and the chronological age of information. Completion of our project will result in a better understanding of information flow and could assist in elucidating the complex mechanisms that underlie a variety of human dynamics and organizations. The analysis will involve studying meme diffusion in large-scale social media by collecting and analyzing massive streams of public micro-blogging data.

The project stands to benefit both the research community and the public significantly. Our data will be made available via APIs and include information on meme propagation networks, statistical data, and relevant user and content features. The open-source platform we develop will be made publicly available and will be extensible to ever more research areas as a greater preponderance of human activities are replicated online. Additionally, we will create a web service open to the public for monitoring trends, bursts, and suspicious memes. This service could mitigate the diffusion of false and misleading ideas, detect hate speech and subversive propaganda, and assist in the preservation of open debate.

NSF grant to date of a little over $900K.

I wonder about a web service to: “… mitigate the diffusion of false and misleading ideas, detect hate speech and subversive propaganda, and assist in the preservation of open debate.”

The definitions of “false and misleading ideas,” as well as “hate speech and subversive propaganda,” vary from community to community.